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AdRAG.py

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+# =============================================================================
+# COPYRIGHT NOTICE
+# -----------------------------------------------------------------------------
+# This source code is the intellectual property of Aditya Pandey.
+# Any unauthorized reproduction, distribution, or modification of this code
+# is strictly prohibited.
+# If you wish to use or modify this code for your project, please ensure
+# to give full credit to Aditya Pandey.
+#
+# PROJECT DESCRIPTION
+# -----------------------------------------------------------------------------
+# This code is for a chatbot crafted with powerful prompts, designed to
+# utilize the Gemini API. It is tailored to assist cybersecurity researchers.
+#
+# Author: Aditya Pandey
+# =============================================================================
+
+# Import library
+import os
+import faiss
+import numpy as np
+import pandas as pd
+import requests
+from PIL import Image
+from PyPDF2 import PdfReader
+import streamlit as st
+from gtts import gTTS
+from io import BytesIO
+import google.generativeai as genai
+from constants import gemini_key
+from bs4 import BeautifulSoup
+import urllib.request
+import re
+import json
+from google.api_core.exceptions import GoogleAPIError
+import speech_recognition as sr
+from collections import defaultdict
+
+# Streamlit configuration
+st.set_page_config(
+    page_title="OxSecure RAG",
+    page_icon="🤿",
+    layout="wide"
+)
+
+def load_css(file_name):
+    with open(file_name) as f:
+        st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
+
+# Load the CSS file
+load_css("ui/Style.css")
+
+# API configuration
+os.environ["GOOGLE_API_KEY"] = gemini_key
+genai.configure(api_key=os.environ['GOOGLE_API_KEY'])
+
+# Function to query Gemini model
+def query_gemini(context, prompt, image=None):
+    try:
+        if image:
+            model = genai.GenerativeModel('gemini-1.5-pro-latest')
+            response = model.generate_content([context + prompt, image])
+        else:
+            model = genai.GenerativeModel('gemini-1.5-pro-latest')
+            response = model.generate_content(context + prompt)
+        
+        if hasattr(response, 'candidates') and response.candidates:
+            return ' '.join(part.text for part in response.candidates[0].content.parts)
+        else:
+            st.error("Unexpected response format from Gemini API.")
+            return None
+    except GoogleAPIError as e:
+        st.error(f"An error occurred while querying the Gemini API: {e}")
+        return None
+
+# Function to extract text from PDF
+def extract_text_from_pdf(file):
+    try:
+        pdf_reader = PdfReader(file)
+        text = ""
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+        return text
+    except Exception as e:
+        st.error(f"An error occurred while extracting text from PDF: {e}")
+        return ""
+
+# Function to extract text from URL
+def extract_text_from_url(url):
+    try:
+        headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'}
+        request = urllib.request.Request(url, headers=headers)
+        response = urllib.request.urlopen(request)
+        html = response.read()
+        soup = BeautifulSoup(html, 'html.parser')
+        paragraphs = soup.find_all('p')
+        text = ' '.join([para.get_text() for para in paragraphs])
+        return text
+    except Exception as e:
+        st.error(f"An error occurred while extracting text from URL: {e}")
+        return ""
+
+# Function to extract text from CSV
+def extract_text_from_csv(file):
+    try:
+        df = pd.read_csv(file)
+        return df.to_string(index=False)
+    except Exception as e:
+        st.error(f"An error occurred while extracting text from CSV: {e}")
+        return ""
+
+# Function to extract text from Excel
+def extract_text_from_excel(file):
+    try:
+        df = pd.read_excel(file)
+        return df.to_string(index=False)
+    except Exception as e:
+        st.error(f"An error occurred while extracting text from Excel: {e}")
+        return ""
+
+# Function to extract text from JSON
+def extract_text_from_json(file):
+    try:
+        json_data = json.load(file)
+        formatted_text = json.dumps(json_data, indent=4)
+        return formatted_text
+    except Exception as e:
+        st.error(f"An error occurred while extracting text from JSON: {e}")
+        return ""
+
+# Remove special characters and improve formatting
+def clean_text(text):
+    # Retain only alphabetic characters, numbers, punctuation, and spaces
+    clean_text = re.sub(r'[^a-zA-Z0-9.,!?;:()\'\" \n]', '', text)
+    return re.sub(r'\s+', ' ', clean_text).strip()
+
+# Placeholder function to create embeddings
+def embed_text(text):
+    # This should be replaced with the actual embedding generation logic
+    # For demonstration, return a dummy vector
+    return np.random.rand(512).astype('float32')
+
+# Function to create embeddings and store in FAISS
+def store_embeddings(text):
+    chunks = [text[i:i+512] for i in range(0, len(text), 512)]
+    vectors = [embed_text(chunk) for chunk in chunks]
+    dimension = vectors[0].shape[0]
+    index = faiss.IndexFlatL2(dimension)
+    index.add(np.array(vectors))
+    return index, chunks
+
+# Function to search embeddings and retrieve relevant text
+def search_embeddings(index, query, top_k):
+    query_vector = embed_text(query)  # Replace with actual embedding generation
+    D, I = index.search(np.array([query_vector]), k=top_k)
+    return I[0]
+
+# Function to handle Q&A
+def handle_qa(query, faiss_index, document_chunks, top_k):
+    if faiss_index:
+        retrieved_indices = search_embeddings(faiss_index, query, top_k)
+        context = " ".join([document_chunks[i] for i in retrieved_indices])
+        response = query_gemini(context, query)
+    else:
+        response = query_gemini(st.session_state.context, query)
+    return response
+
+# Function for speech recognition
+def recognize_speech():
+    r = sr.Recognizer()
+    try:
+        with sr.Microphone() as source:
+            st.info("Listening...")
+            audio = r.listen(source)
+            text = r.recognize_google(audio)
+            st.success(f"You said: {text}")
+            return text
+    except sr.UnknownValueError:
+        st.error("Could not understand audio")
+        return None
+    except sr.RequestError as e:
+        st.error(f"Could not request results from Google Speech Recognition service; {e}")
+        return None
+    except Exception as e:
+        st.error(f"An error occurred: {e}")
+        return None
+
+# Function to analyze log file
+def analyze_log_file(file):
+    log_summary = {
+        'total_lines': 0,
+        'error_count': 0,
+        'warning_count': 0,
+        'info_count': 0,
+        'error_details': defaultdict(int),
+        'warning_details': defaultdict(int),
+        'info_details': defaultdict(int),
+    }
+
+    error_pattern = re.compile(r'\bERROR\b')
+    warning_pattern = re.compile(r'\bWARNING\b')
+    info_pattern = re.compile(r'\bINFO\b')
+
+    with open(file, 'r') as file:
+        for line in file:
+            log_summary['total_lines'] += 1
+
+            if error_pattern.search(line):
+                log_summary['error_count'] += 1
+                log_summary['error_details'][line.strip()] += 1
+            elif warning_pattern.search(line):
+                log_summary['warning_count'] += 1
+                log_summary['warning_details'][line.strip()] += 1
+            elif info_pattern.search(line):
+                log_summary['info_count'] += 1
+                log_summary['info_details'][line.strip()] += 1
+
+    return log_summary
+
+# Main App Function
+def render_main_app():
+    st.title('OxSecure RAG ♨️')
+    st.divider()
+    st.markdown('**By :- Aditya Pandey 🧑🏻‍💻**')
+
+    input_prompt = st.text_input("Input Prompt: ", key="input")
+
+    uploaded_file = st.file_uploader("Choose a file (image, PDF, CSV, Excel, JSON, or LOG)...", type=["jpg", "jpeg", "png", "pdf", "csv", "xlsx", "json", "log"])
+    uploaded_url = st.text_input("Or enter an article URL:")
+
+    image = None
+    file_text = ""
+
+    if uploaded_file is not None:
+        if uploaded_file.type in ["image/jpeg", "image/png", "image/jpg", "image/webp"]:
+            image = Image.open(uploaded_file)
+            st.image(image, caption="Uploaded Image.", use_column_width=True)
+        elif uploaded_file.type == "application/pdf":
+            file_text = extract_text_from_pdf(uploaded_file)
+            st.text_area("Extracted Text from PDF:", file_text, height=300)
+        elif uploaded_file.type == "text/csv":
+            df = pd.read_csv(uploaded_file)
+            st.dataframe(df)
+            file_text = df.to_string(index=False)
+        elif uploaded_file.type == "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet":
+            df = pd.read_excel(uploaded_file)
+            st.dataframe(df)
+            file_text = df.to_string(index=False)
+        elif uploaded_file.type == "application/json":
+            df = pd.read_json(uploaded_file)
+            st.json(df.to_dict())
+            file_text = df.to_string(index=False)
+        elif uploaded_file.type == "text/plain":
+            if uploaded_file.name.endswith(".log"):
+                file_text = uploaded_file.read().decode("utf-8")
+                log_summary = analyze_log_file(file_text.splitlines())
+                st.write("Log Summary:")
+                st.write(f"Total Lines: {log_summary['total_lines']}")
+                st.write(f"Error Count: {log_summary['error_count']}")
+                st.write(f"Warning Count: {log_summary['warning_count']}")
+                st.write(f"Info Count: {log_summary['info_count']}")
+
+                st.write("\nError Details:")
+                for error, count in log_summary['error_details'].items():
+                    st.write(f"{count} occurrence(s): {error}")
+
+                st.write("\nWarning Details:")
+                for warning, count in log_summary['warning_details'].items():
+                    st.write(f"{count} occurrence(s): {warning}")
+
+                st.write("\nInfo Details:")
+                for info, count in log_summary['info_details'].items():
+                    st.write(f"{count} occurrence(s): {info}")
+            else:
+                st.error("Please upload a valid log file.")
+        else:
+            st.error("Unsupported file type.")
+    elif uploaded_url:
+        file_text = extract_text_from_url(uploaded_url)
+        st.text_area("Extracted Text from URL:", file_text, height=300)
+
+    # Initialize or update session state for context
+    if "context" not in st.session_state:
+        st.session_state.context = ""
+    if "faiss_index" not in st.session_state:
+        st.session_state.faiss_index = None
+    if "document_chunks" not in st.session_state:
+        st.session_state.document_chunks = []
+
+    def clear_previous_data():
+        st.session_state.faiss_index = None
+        st.session_state.document_chunks = []
+        st.session_state.context = ""
+
+    submit = st.button("Start Deep Diving 🤿", key="start_button")
+
+    if submit:
+        if input_prompt or file_text:
+            clear_previous_data()
+            
+            prompt = input_prompt if input_prompt else ""
+            st.session_state.context += " " + file_text  # Update the context with new extracted text
+            
+            if file_text:
+                st.session_state.faiss_index, st.session_state.document_chunks = store_embeddings(file_text)
+            
+            # Start spinner before processing
+            spinner = st.spinner("Processing..... Getting Results ⏳")
+            with spinner:
+                response = query_gemini(st.session_state.context, prompt, image)
+            
+            # Stop spinner after processing
+            if response:
+                st.subheader("Extracted Data 📡")
+                st.write(response)
+                
+                clean_response = clean_text(response)
+
+                # Text-to-Speech conversion
+                tts = gTTS(clean_response)
+                audio_file = BytesIO()
+                tts.write_to_fp(audio_file)
+                st.audio(audio_file, format='audio/mp3')
+        else:
+            st.warning("Please provide an input prompt or upload a file.")
+
+    # Q&A section with slider and radio button
+    st.markdown("-----")
+    st.markdown("**Q/A Section 🤔**")
+
+    query = st.text_input("Enter your query:", key="qa_query")
+    top_k = st.slider("Select the number of document chunks to retrieve:", min_value=1, max_value=10, value=5, step=1)
+    response_mode = st.radio("Select response mode:", ("Text", "Text-to-Speech"))
+
+    qa_button = st.button("Ask", key="qa_button")
+    
+    if qa_button:
+        if query:
+            spinner = st.spinner("Processing your query...")
+            with spinner:
+                response = handle_qa(query, st.session_state.faiss_index, st.session_state.document_chunks, top_k)
+            if response:
+                st.divider()
+                st.markdown("**Q&A Response 🤖**")
+                
+                clean_response = clean_text(response)
+                
+                if response_mode == "Text":
+                    st.write(response)
+                else:
+                    st.write(response)
+                    tts = gTTS(clean_response)
+                    audio_file = BytesIO()
+                    tts.write_to_fp(audio_file)
+                    st.audio(audio_file, format='audio/mp3')
+        else:
+            st.warning("Please enter a query to ask.")
+    
+    st.markdown("-----")
+    
+    # Voice recognition section
+#    st.markdown("**Voice Input 🗣️**")
+#    query = recognize_speech()
+#    if st.button("Start Voice Recognition") and query:
+#        with st.spinner("Processing your voice query..."):
+#            response = handle_qa(query, st.session_state.faiss_index, st.session_state.document_chunks, top_k)
+#        if response:
+#            st.divider()
+#            st.markdown("**Voice Q&A Response 🤖**")
+            
+#            clean_response = clean_text(response)
+#            st.write(clean_response)
+#            tts = gTTS(clean_response)
+#            audio_file = BytesIO()
+#            tts.write_to_fp(audio_file)
+#            st.audio(audio_file, format='audio/mp3')
+#    st.markdown("---")
+    linkedin_url = "https://www.linkedin.com/in/aditya-pandey-896109224"
+    st.markdown(f"Created with 🤗 💖 By Aditya Pandey [ LinkedIn 🔗 ]({linkedin_url})")
+
+# Description and Framework Section
+def render_description_and_framework():
+    st.title("OxSecure RAG - Description and Framework")
+    st.markdown("----")
+    st.markdown("""
+    ## 🚀 ***Project Description*** 
+    ----------------
+    **OxSecure RAG** is your cybersecurity research companion! Powered by the Gemini API and crafted with smart prompts, it can analyze various documents, extract key insights, create embeddings, and support question-answering (Q&A) like never before. 🔍🛡️
+    
+     🛠️ ***Framework Used***
+    - **Streamlit**: The sleek and interactive interface 🎨.
+    - **FAISS**: Super-efficient similarity search and clustering for dense vectors ⚡.
+    - **Pandas**: Handling and processing data files like a pro (CSV, Excel) 📊.
+    - **PyPDF2**: Extracting text from PDFs with ease 📄.
+    - **BeautifulSoup**: Scraping web data with precision 🌐.
+    - **gTTS**: Giving the bot a voice with text-to-speech 🎙️.
+    - **Google Generative AI (genai)**: Querying the powerful Gemini API 🧠.
+    - **SpeechRecognition**: Turning your voice into input for hands-free interaction 🎧.
+                
+    ----------------
+
+     🏗️ ***Architecture***
+    1. **Input Handling**:
+        - Upload various file types (PDF, CSV, Excel, JSON) or provide a URL 🔗.
+        - Input text prompts directly 📝.
+        - Speak your query using voice recognition 🎤.
+    2. **Text Extraction**:
+        - Extract text from uploaded files or URLs using the right tools 📄🌐.
+    3. **Text Embedding**:
+        - Split extracted text into chunks and convert them into embeddings 🧩.
+        - Store embeddings in a FAISS index for fast, relevant search results 🚀.
+    4. **Q&A System**:
+        - Ask questions based on uploaded or entered context ❓.
+        - Retrieve relevant text chunks from the FAISS index and query the Gemini API 🔍.
+    5. **Response Generation**:
+        - View the response from the Gemini API 🧑‍💻.
+        - Convert the response to speech for audio playback 🔊.
+                
+    ----------------
+
+     📋 ***Instructions for Use***
+    1. **Input**:
+        - Upload a file (PDF, CSV, Excel, or JSON), provide a URL, or enter a text prompt 💻.
+    2. **Processing**:
+        - Click "Start Deep Diving" to process the input and extract valuable insights 💡.
+    3. **Q&A**:
+        - Enter a query, choose how many document chunks to retrieve, and select response mode (Text or Text-to-Speech) 🎯.
+        - Click "Ask" to get your answer 🧠.
+    4. **Voice Input**:
+        - Use "Start Voice Recognition" to ask a question verbally 🎙️.
+        - The answer will be generated and spoken aloud 🗣️.
+    5. **Results**:
+        - View extracted data and responses in a clear, readable format 📑.
+        - If Text-to-Speech is selected, listen to the response 🎧.
+    """)
+
+    if st.button("Go to Main App", key="description_go_to_main_app"):
+        st.session_state.show_main_app = True
+        st.experimental_rerun()
+        
+    st.markdown("---")
+    linkedin_url = "https://www.linkedin.com/in/aditya-pandey-896109224"
+    st.markdown(f"Created with 🤗 💖 By Aditya Pandey [ LinkedIn 🔗 ]({linkedin_url})")
+
+# Initialize the app with the description and framework
+if "show_main_app" not in st.session_state:
+    st.session_state.show_main_app = False
+
+if st.session_state.show_main_app:
+    render_main_app()
+else:
+    render_description_and_framework()
+

ImaGen.py

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+import os
+import streamlit as st
+from PIL import Image
+import google.generativeai as genai
+from constants import gemini_key
+
+# Streamlit framework configuration
+st.set_page_config(
+    page_title="OxSecure Images",
+    page_icon="🎨",
+    layout="wide"
+)
+
+# API configuration
+os.environ["GOOGLE_API_KEY"] = gemini_key
+genai.configure(api_key=os.environ['GOOGLE_API_KEY'])
+
+# Function to load Gemini vision model and get responses
+def get_gemini_response(input_text, image=None):
+    model = genai.GenerativeModel('gemini-pro-vision')
+    if image is not None:
+        response = model.generate_content([input_text, image])
+    else:
+        response = model.generate_content(input_text)
+    return response.text
+
+def generate_gemini_image(prompt, num_images=1, size="1024x1024"):
+    model = genai.GenerativeModel('gemini-pro-vision')
+    # Placeholder for image generation method; replace with actual method
+    response = model.generate_content(prompt)  # This line should be replaced with the correct method for image generation
+    # Assuming the API returns a list of images as URLs or base64 encoded strings
+    return response.images[:num_images]  # Adjust this line based on actual API response format
+
+# Streamlit Main Framework
+st.header('OxSecure ImaGen 🎨')
+st.title('GenAI ImaGen powers ♨️')
+st.subheader('By :- Aadi 🧑‍💻')
+
+# Text input for prompt
+input_text = st.text_input("Input Prompt: ", key="input")
+
+# File uploader for image
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+image = None
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image.", use_column_width=True)
+
+# Button to get response about the image
+submit_analyze = st.button("Tell me about the image")
+if submit_analyze:
+    if input_text and image is not None:
+        response = get_gemini_response(input_text, image)
+    elif image is not None:
+        response = get_gemini_response("", image)
+    elif input_text:
+        response = get_gemini_response(input_text)
+    else:
+        response = "Please provide an input prompt or upload an image."
+    st.subheader("The Response is")
+    st.write(response)
+
+# Button to generate an image from a prompt
+submit_generate = st.button("Generate Image from Prompt")
+if submit_generate and input_text:
+    num_images = st.number_input("Number of Images to Generate", min_value=1, max_value=5, value=1)
+    size = st.selectbox("Select Image Size", ["512x512", "1024x1024", "2048x2048"], index=1)
+    generated_images = generate_gemini_image(input_text, num_images=num_images, size=size)
+    for img in generated_images:
+        st.image(img, caption="Generated Image", use_column_width=True)
+else:
+    if not input_text:
+        st.write("Please provide an input prompt to generate an image.")

app.py

@@ -0,0 +1,276 @@
+# =============================================================================
+# COPYRIGHT NOTICE
+# -----------------------------------------------------------------------------
+# This source code is the intellectual property of Aditya Pandey.
+# Any unauthorized reproduction, distribution, or modification of this code
+# is strictly prohibited.
+# If you wish to use or modify this code for your project, please ensure
+# to give full credit to Aditya Pandey.
+#
+# PROJECT DESCRIPTION
+# -----------------------------------------------------------------------------
+# This code is for a chatbot crafted with powerful prompts, designed to
+# utilize the Gemini API. It is tailored to assist cybersecurity researchers.
+#
+# Author: Aditya Pandey
+# =============================================================================
+import os
+import streamlit as st
+from PIL import Image
+import textwrap
+from constants import gemini_key
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.llms import OpenAI
+from langchain import PromptTemplate
+import seaborn as sns
+from langchain.chains import LLMChain
+import google.generativeai as genai
+from langchain.memory import ConversationBufferMemory
+from google.generativeai.types import HarmCategory, HarmBlockThreshold, HarmProbability
+from google.generativeai import GenerativeModel
+from langchain.chains import SequentialChain
+
+#API configuration
+os.environ["GOOGLE_API_KEY"]=gemini_key
+genai.configure(api_key = os.environ['GOOGLE_API_KEY'])
+
+# Define correct username and password
+CORRECT_USERNAME = "Oxsecure"
+CORRECT_PASSWORD = "Oxsecure@123"
+
+# streamlit framework
+st.set_page_config(
+    page_title="OxSecure",
+    page_icon="🔒",
+    layout="wide"
+)
+
+# Load custom CSS
+def load_css(file_name):
+    with open(file_name) as f:
+        st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
+
+# Load the CSS file
+load_css("ui/Style.css")
+
+
+def render_login_page():
+    st.title("Oxsecure 🧠 - Your Companion! 🔒")
+    st.markdown("---")
+    st.image('ui/Ox.jpg', width=200, use_column_width='always')
+    st.write("Unlock the realm of cybersecurity expertise with OxSecure 🧠 🚀 Safeguarding your data. 🔒 Let's chat about security topics and empower your knowledge! Product of CyberBULL 👁️")
+    st.markdown("---")
+    st.write("Please log in to continue.")
+    st.write("💳 Default Credentials  Username = Oxsecure , Password = Oxsecure@123 ")
+    st.divider()
+    st.markdown("""
+    **Welcome to OxSecure Intelligence** 🔐 your ultimate destination for comprehensive and up-to-date information on cybersecurity. Whether you're a professional, student, or enthusiast, this app is designed to empower you with the knowledge and tools needed to navigate the complex world of cybersecurity.
+
+    **Features**
+
+    **📖 In-Depth Information on Cybersecurity Topics:**
+    
+    Explore a wide range of topics in cybersecurity with detailed articles and guides. This app covers everything from basic concepts to advanced techniques, ensuring you have access to the information you need to stay informed and secure.
+
+    **💻 Secure Coding Principles:**
+    
+    Learn the best practices for secure coding to protect your software from vulnerabilities. These guides provide practical tips and examples to help you write code that is both functional and secure.
+
+    **🚨 Major Cyberattacks:**
+    
+    Stay updated on major cyberattacks and learn from real-world cases. Understand the methods used by attackers, the impact of these attacks, and the measures you can take to protect yourself and your organization.
+
+    **⚙️ Security Misconfiguration:**
+    
+    Identify common security misconfigurations and learn how to fix them. These resources help you ensure that your systems are configured correctly to prevent breaches and unauthorized access.
+
+    **🔎 VirusTotal File Analysis:**
+    
+    Upload your files for in-depth malware scanning using the VirusTotal API. Instantly analyze your files and receive reports with threat intelligence on potential malware, ensuring your files are clean and secure.
+
+    **🔐 Comprehensive File Analysis:**
+    
+    Use this app to scan a variety of file types like PDFs, images, executables, and logs. From extracting metadata to analyzing file content, OxSecure Intelligence ensures thorough and real-time security analysis.
+
+    **🤖 Powered by Gemini LLM:**
+    
+    This app leverages the powerful Gemini LLM to provide you with accurate and relevant information. Gemini LLM enhances the content with cutting-edge insights and helps you get the answers you need quickly and efficiently.
+
+    **🖼️ Image Analysis with Imagen:**
+    
+    Utilize the Imagen feature to extract detailed information from images. Simply upload an image, and our app will analyze it and provide responses tailored to your queries. Perfect for identifying vulnerabilities, assessing security measures, and more.
+
+    **Why Choose OxSecure Intelligence?**
+
+    - **🌐 Comprehensive Coverage:** From basic concepts to advanced practices, this app covers all aspects of cybersecurity.
+    - **📚 Expert Guidance:** Learn from detailed articles and guides written by cybersecurity experts.
+    - **⚡ Advanced Tools:** Use powerful AI tools like Gemini LLM, Imagen, and VirusTotal to enhance your learning and problem-solving capabilities.
+    - **🔄 Stay Updated:** Keep up with the latest trends, threats, and best practices in the cybersecurity field.
+
+    Join OxSecure Intelligence today and take your cybersecurity knowledge to the next level! 🚀
+    """)
+    st.markdown("---")
+    linkedin_url = "https://www.linkedin.com/in/aditya-pandey-896109224"
+    st.markdown("  Created with 🤗💖 By Aditya Pandey  " f"[  LinkedIn 🔗]({linkedin_url})")
+
+    username = st.sidebar.text_input("Username 👤")
+    password = st.sidebar.text_input("Password 🔑", type="password")
+    login_button = st.sidebar.button("Login 🫢")
+
+    if login_button:
+        if username == CORRECT_USERNAME and password == CORRECT_PASSWORD:
+            st.session_state.authenticated = True
+            st.success("Login successful!")
+            st.experimental_rerun()
+            render_main_program()
+        else:
+            st.error("Invalid username or password. Please try again.")
+
+## Function to load Gemini vision model and get response
+def get_gemini_response(input_prompt, image):
+    Model = genai.GenerativeModel('gemini-1.5-pro')
+    if input_prompt != "":
+        response = Model.generate_content([input_prompt, image])
+    else:
+        response = Model.generate_content(image)
+    return response.text
+
+def render_main_program():
+    st.markdown("# 🔒 Unlock the Future of Cybersecurity with OxSecure ")
+    st.divider()
+    st.markdown("**Where Knowledge Meets Innovation! 🚀 Dive into Cyber Brilliance with OxSecure** 🤖 🌟")
+    st.markdown("----")
+    app_choice = st.sidebar.radio("Choose App", ("OxSecure Chat 🤖", "OxSecure ImaGen 🎨"))
+
+    if app_choice == "OxSecure Chat 🤖":
+        render_gemini_api_app()
+    elif app_choice == "OxSecure ImaGen 🎨":
+        render_gemini_vision_app()
+
+def render_gemini_api_app():
+    st.caption("🚀 Empower Tomorrow, 🛡️ Secure Today: Unleash the Power of Cybersecurity Brilliance! 💻✨ 🛡️💬  ")
+    st.markdown("---")
+
+    st.title("OxSecure Intelligence 🧠")
+    st.markdown("-----")
+    input_text = st.text_input("Search your Security Related Topic 🔍")
+
+    # Prompt Templates
+    first_input_prompt = PromptTemplate(
+        input_variables=['Topic'],
+        template = textwrap.dedent("""
+            As an experienced cybersecurity researcher, provide a comprehensive and detailed explanation about {Topic}. Cover the following aspects:
+            1. Introduction and Importance in well informative
+            2. Key Concepts and Terminologies
+            3. Historical Background and Evolution
+            4. Its Architecture and Types
+            5. Current Trends and Best Practices
+            6. Major Threats and Vulnerabilities
+            7. Case Studies and Real-world Examples
+            8. Future Outlook and Predictions
+            
+            Ensure the information is professional, well-structured, key conceptual  and suitable for someone with an advanced understanding and Beginner of cybersecurity.
+        """)
+    )
+
+    # Select the model
+    model = genai.GenerativeModel('gemini-1.5-pro')
+    safety_settings = {
+        HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_DANGEROUS: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_SEXUAL: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_TOXICITY: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmProbability.HIGH
+    }
+
+    # Memory
+    Topic_memory = ConversationBufferMemory(input_key='Topic', memory_key='chat_history')
+    Policy_memory = ConversationBufferMemory(input_key='secure coding', memory_key='chat_history')
+    Practice_memory = ConversationBufferMemory(input_key='Practice', memory_key='description_history')
+
+    ## GEMINI LLMS
+    llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
+    chain = LLMChain(
+        llm=llm, prompt=first_input_prompt, verbose=True, output_key='secure coding', memory=Topic_memory)
+    safety_settings = {
+        HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_DANGEROUS: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_SEXUAL: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_TOXICITY: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmProbability.HIGH
+    }
+
+    # Prompt Templates
+    second_input_prompt = PromptTemplate(
+        input_variables=['secure coding'],
+        template="write best {secure coding} and perfect code snippet for implementing secure coding to this {Topic} in well detailed and descriptive way use code snippets for each point and describe code."
+    )
+
+    chain2 = LLMChain(
+        llm=llm, prompt=second_input_prompt, verbose=True, output_key='Practice', memory=Policy_memory)
+    # Prompt Templates
+    third_input_prompt = PromptTemplate(
+        input_variables=['Practice'],
+        template="Implement major best Cybersecurity {Practice} for this {Topic} that helps better security postures into any business. illustrate Major cyberattack which is done by misconfiguration of {Topic} and give the informative info about the malware which caused this"
+    )
+    chain3 = LLMChain(llm=llm, prompt=third_input_prompt, verbose=True, output_key='description', memory=Practice_memory)
+    parent_chain = SequentialChain(
+        chains=[chain, chain2, chain3], input_variables=['Topic'], output_variables=['secure coding', 'Practice',
+                                                                                     'description'], verbose=True)
+
+    if input_text:
+        with st.spinner('Processing.... ⏳'):
+            st.text(parent_chain({'Topic': input_text}))
+
+        with st.expander('Your Topic'):
+            st.info(Topic_memory.buffer)
+
+        with st.expander('Major Practices'):
+            st.info(Practice_memory.buffer)
+    st.markdown("---")
+    linkedin_url = "https://www.linkedin.com/in/aditya-pandey-896109224"
+    st.markdown("  Created with 🤗💖 By Aditya Pandey   "  f"[  LinkedIn 🔗]({linkedin_url})")
+
+def render_gemini_vision_app():
+    st.title('OxSecure ImaGen 🎨')
+    st.markdown("----")
+    input_prompt = st.text_input("Input Prompt: ", key="input")
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+    image = ""
+    submit = False  # Initialize submit variable
+
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image.", use_column_width=True)
+        submit = st.button("Tell me about the image")
+
+    if submit:
+        response = get_gemini_response(input_prompt, image)
+        st.subheader("The Response is")
+        st.write(response)
+
+    st.markdown("---")
+    linkedin_url = "https://www.linkedin.com/in/aditya-pandey-896109224"
+    st.markdown("  Created with 🤗💖 By Aditya Pandey  " f"[  LinkedIn 🔗]({linkedin_url})")
+
+def main():
+    # Initialize session state
+    if 'authenticated' not in st.session_state:
+        st.session_state.authenticated = False
+
+    # If not authenticated, display login portal
+    if not st.session_state.authenticated:
+        render_login_page()
+    else:
+        render_main_program()
+
+if __name__ == "__main__":
+    main()

constants.py

@@ -0,0 +1,2 @@
+openai_key="sk-----"
+gemini_key="AIzaSyD8DucjSYpGMiOTignA_okKlnmAEqhPyzw"

mod2.py

@@ -0,0 +1,143 @@
+## Integrate our code GEMINI API
+import os
+import pathlib
+import textwrap
+from PIL import Image
+from constants import gemini_key
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain import PromptTemplate
+from langchain.chains import LLMChain
+
+import google.generativeai as genai
+
+from langchain.memory import ConversationBufferMemory
+from google.generativeai import GenerativeModel
+from google.generativeai.types import HarmCategory, HarmBlockThreshold, HarmProbability
+from langchain.chains import SequentialChain
+
+import streamlit as st
+
+# streamlit framework
+st.set_page_config(
+    page_title="OxSecure A.I",
+    page_icon="🔒",
+    layout="wide"
+)
+
+# Load custom CSS
+def load_css(file_name):
+    with open(file_name) as f:
+        st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
+
+# Load the CSS file
+load_css("ui/Style.css")
+
+#API configuration
+
+os.environ["GOOGLE_API_KEY"]=gemini_key
+genai.configure(api_key = os.environ['GOOGLE_API_KEY'])
+
+## Function to load OpenAI model and get respones
+
+def get_gemini_response(input, image):
+    model = genai.GenerativeModel('gemini-1.5-pro-latest')
+    if input != "":
+        response = model.generate_content(
+            [input, image],
+            safety_settings={
+                HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
+                HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
+                HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
+                HarmProbability:HarmBlockThreshold.BLOCK_NONE,
+                HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE
+            }
+        )
+    else:
+        response = model.generate_content(
+            image,
+            safety_settings={
+                HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
+                HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
+                HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
+                HarmProbability:HarmBlockThreshold.BLOCK_NONE,
+                HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE
+            }
+        )
+    return response.text
+
+
+st.title('OxSecure Intelligence 🧠')
+st.caption('Cybersecurity Best practices for Infrastructure')
+st.subheader('By :- Aadi 🧑‍💻')
+st.text('🚀 Empower Tomorrow, 🛡️ Secure Today: Unleash the Power of Cybersecurity Brilliance! 💻✨ ')
+input_text=st.text_input("Search Your Desire Security Related Topic 🔍")
+input=st.text_input("Input Prompt: ",key="input")
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+image=""
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image.", use_column_width=True)
+submit=st.button("Tell me about the image")
+if submit:
+    
+    response=get_gemini_response(input,image)
+    st.subheader("The Response is")
+    st.write(response)
+
+# Prompt Templates
+
+first_input_prompt=PromptTemplate(
+    input_variables=['Topic'],
+    template="Tell me everything about and explain in so informative descriptive way about {Topic} "
+)
+
+# Memory
+
+Topic_memory = ConversationBufferMemory(input_key='Topic', memory_key='chat_history')
+Policy_memory = ConversationBufferMemory(input_key='security policies', memory_key='chat_history')
+Practice_memory = ConversationBufferMemory(input_key='Practice', memory_key='description_history')
+
+# GEMINI LLMS
+llm = ChatGoogleGenerativeAI(
+    model="gemini-1.5-pro-latest",
+    safety_settings={
+        HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
+        HarmProbability:HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE
+    }
+)
+chain=LLMChain(llm=llm,prompt=first_input_prompt,verbose=True,output_key='security policies',memory=Topic_memory)
+
+# Prompt Templates
+
+second_input_prompt=PromptTemplate(
+    input_variables=['security policies'],
+    template="write best {security policies} and perfect code snippet for implementing secure coding to this {Topic} and give me all important full secure coding principles about {Topic} use codes snippet for every countersome points . "
+)
+chain2=LLMChain(
+    llm=llm,prompt=second_input_prompt,verbose=True,output_key='Practice',memory=Policy_memory)
+# Prompt Templates
+
+third_input_prompt=PromptTemplate(
+    input_variables=['Practice'],
+    template="Implement  5 major best Cybersecurity {Practice} for this {Topic} that helps better security postures into infrastructure business. give Major cyberattack which is done by this {Topic} and write about malware which is developed by this {Topic}"
+)
+chain3=LLMChain(llm=llm,prompt=third_input_prompt,verbose=True,output_key='description',memory=Practice_memory)
+parent_chain=SequentialChain(
+    chains=[chain,chain2,chain3],input_variables=['Topic'],output_variables=['security policies','Practice','description'],verbose=True)
+
+
+
+if input_text:
+    st.text(parent_chain({'Topic':input_text}))
+
+    with st.expander('Your Topic'): 
+        st.info(Topic_memory.buffer)
+
+    with st.expander('Major Practices'): 
+        st.info(Practice_memory.buffer)
+st.markdown("---")
+st.markdown("                           Created with ❤️ by Aditya Pandey ")
+

packages.txt

@@ -0,0 +1 @@
+portaudio19-dev

requirements.txt

@@ -0,0 +1,95 @@
+aiohttp==3.9.1
+aiosignal==1.3.1
+altair==5.2.0
+annotated-types==0.6.0
+anyio==4.2.0
+attrs==23.2.0
+blinker==1.7.0
+bs4
+cachetools==5.3.2
+chardet
+certifi==2023.11.17
+charset-normalizer==3.3.2
+click==8.1.7
+dataclasses-json==0.6.3
+distro==1.9.0
+frozenlist==1.4.1
+faiss-cpu
+gitdb==4.0.11
+gtts
+GitPython==3.1.41
+google-ai-generativelanguage==0.4.0
+google-api-core==2.15.0
+google-auth==2.26.2
+google-generativeai==0.3.2
+googleapis-common-protos==1.62.0
+greenlet==3.0.3
+grpcio==1.60.0
+grpcio-status==1.60.0
+h11==0.14.0
+httpcore==1.0.2
+httpx==0.26.0
+idna==3.6
+importlib-metadata==7.0.1
+Jinja2==3.1.3
+jsonpatch==1.33
+jsonpointer==2.4
+jsonschema==4.21.0
+jsonschema-specifications==2023.12.1
+langchain==0.1.1
+langchain-community==0.0.13
+langchain_google_genai
+langchain-core==0.1.12
+langsmith==0.0.83
+markdown-it-py==3.0.0
+MarkupSafe==2.1.3
+marshmallow==3.20.2
+mdurl==0.1.2
+multidict==6.0.4
+mypy-extensions==1.0.0
+numpy==1.26.3
+openai==1.8.0
+packaging==23.2
+pandas==2.1.4
+Pillow==10.2.0
+proto-plus==1.23.0
+protobuf==4.25.2
+pyarrow==14.0.2
+pyasn1==0.5.1
+pyasn1-modules==0.3.0
+pydantic==2.5.3
+pydantic_core==2.14.6
+pydeck==0.8.1b0
+PyPDF2
+Pygments==2.17.2
+python-dateutil==2.8.2
+python-dotenv==1.0.0
+pytz==2023.3.post1
+PyYAML==6.0.1
+referencing==0.32.1
+requests==2.31.0
+rich==13.7.0
+rpds-py==0.17.1
+rsa==4.9
+six==1.16.0
+smmap==5.0.1
+sniffio==1.3.0
+SQLAlchemy==2.0.25
+streamlit==1.34.0
+tenacity==8.2.3
+toml==0.10.2
+toolz==0.12.0
+tornado==6.4
+tqdm==4.66.1
+typing-inspect==0.9.0
+typing_extensions==4.9.0
+tzdata==2023.4
+tzlocal==5.2
+urllib3==2.1.0
+validators==0.22.0
+watchdog==3.0.0
+yarl==1.9.4
+zipp==3.17.0
+SpeechRecognition
+pydub
+pyaudio

test.py

@@ -0,0 +1,842 @@
+# =============================================================================
+# COPYRIGHT NOTICE
+# -----------------------------------------------------------------------------
+# This source code is the intellectual property of Aditya Pandey.
+# Any unauthorized reproduction, distribution, or modification of this code
+# is strictly prohibited.
+# If you wish to use or modify this code for your project, please ensure
+# to give full credit to Aditya Pandey.
+#
+# PROJECT DESCRIPTION
+# -----------------------------------------------------------------------------
+# This code is for a chatbot crafted with powerful prompts, designed to
+# utilize the Gemini API. It is tailored to assist cybersecurity researchers.
+#
+# Author: Aditya Pandey
+# =============================================================================
+
+import os
+import streamlit as st
+from PIL import Image
+import textwrap
+from io import BytesIO
+import io
+import chardet
+from constants import gemini_key
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.llms import OpenAI
+from langchain import PromptTemplate
+from langchain.chains import LLMChain
+import google.generativeai as genai
+from langchain.memory import ConversationBufferMemory
+from google.generativeai.types import HarmCategory, HarmBlockThreshold, HarmProbability
+from google.generativeai import GenerativeModel
+from langchain.chains import SequentialChain
+from datetime import datetime
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+import numpy as np
+import requests
+from pefile import PE, PEFormatError
+import re
+import hashlib
+
+# VirusTotal API details
+VIRUSTOTAL_API_KEY = 'ed48e6407e0b7975be7d19c797e1217f500183c9ae84d1119af8628ba4c98c3d'
+
+
+# API configuration
+os.environ["GOOGLE_API_KEY"] = gemini_key
+genai.configure(api_key=os.environ['GOOGLE_API_KEY'])
+
+# Define correct username and password
+CORRECT_USERNAME = "Oxsecure"
+CORRECT_PASSWORD = "Oxsecure@123"
+
+# Streamlit framework
+st.set_page_config(
+    page_title="OxSecure",
+    page_icon="🔒",
+    layout="wide"
+)
+
+# Load custom CSS
+def load_css(file_name):
+    with open(file_name) as f:
+        st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
+
+# Load the CSS file
+load_css("ui/Style.css")
+
+def render_login_page():
+    st.title("Oxsecure 🧠 - Your Companion! 🔒")
+    st.markdown("---")
+    st.image('ui/Ox.jpg', width=200, use_column_width='always')
+    st.write("Unlock the realm of cybersecurity expertise with OxSecure 🧠 🚀 Safeguarding your data. 🔒 Let's chat about security topics and empower your knowledge! Product of CyberBULL 👁️")
+    st.markdown("---")
+    st.write("Please log in to continue.")
+    st.write("💳 Default Credentials  Username = Oxsecure , Password = Oxsecure@123 ")
+    st.divider()
+    st.markdown("""
+    **Welcome to OxSecure Intelligence** 🔐 your ultimate destination for comprehensive and up-to-date information on cybersecurity. Whether you're a professional, student, or enthusiast, this app is designed to empower you with the knowledge and tools needed to navigate the complex world of cybersecurity.
+
+    **Features**
+
+    **📖 In-Depth Information on Cybersecurity Topics:**
+    
+    Explore a wide range of topics in cybersecurity with detailed articles and guides. This app covers everything from basic concepts to advanced techniques, ensuring you have access to the information you need to stay informed and secure.
+
+    **💻 Secure Coding Principles:**
+    
+    Learn the best practices for secure coding to protect your software from vulnerabilities. These guides provide practical tips and examples to help you write code that is both functional and secure.
+
+    **🚨 Major Cyberattacks:**
+    
+    Stay updated on major cyberattacks and learn from real-world cases. Understand the methods used by attackers, the impact of these attacks, and the measures you can take to protect yourself and your organization.
+
+    **⚙️ Security Misconfiguration:**
+    
+    Identify common security misconfigurations and learn how to fix them. These resources help you ensure that your systems are configured correctly to prevent breaches and unauthorized access.
+
+    **🔎 VirusTotal File Analysis:**
+    
+    Upload your files for in-depth malware scanning using the VirusTotal API. Instantly analyze your files and receive reports with threat intelligence on potential malware, ensuring your files are clean and secure.
+
+    **🔐 Comprehensive File Analysis:**
+    
+    Use this app to scan a variety of file types like PDFs, images, executables, and logs. From extracting metadata to analyzing file content, OxSecure Intelligence ensures thorough and real-time security analysis.
+
+    **🤖 Powered by Gemini LLM:**
+    
+    This app leverages the powerful Gemini LLM to provide you with accurate and relevant information. Gemini LLM enhances the content with cutting-edge insights and helps you get the answers you need quickly and efficiently.
+
+    **🖼️ Image Analysis with Imagen:**
+    
+    Utilize the Imagen feature to extract detailed information from images. Simply upload an image, and our app will analyze it and provide responses tailored to your queries. Perfect for identifying vulnerabilities, assessing security measures, and more.
+
+    **Why Choose OxSecure Intelligence?**
+
+    - **🌐 Comprehensive Coverage:** From basic concepts to advanced practices, this app covers all aspects of cybersecurity.
+    - **📚 Expert Guidance:** Learn from detailed articles and guides written by cybersecurity experts.
+    - **⚡ Advanced Tools:** Use powerful AI tools like Gemini LLM, Imagen, and VirusTotal to enhance your learning and problem-solving capabilities.
+    - **🔄 Stay Updated:** Keep up with the latest trends, threats, and best practices in the cybersecurity field.
+
+    Join OxSecure Intelligence today and take your cybersecurity knowledge to the next level! 🚀
+    """)
+    st.markdown("---")
+    linkedin_url = "https://www.linkedin.com/in/aditya-pandey-896109224"
+    st.markdown("  Created with 🤗💖 By Aditya Pandey  " f"[  LinkedIn 🔗]({linkedin_url})")
+
+    username = st.sidebar.text_input("Username 👤")
+    password = st.sidebar.text_input("Password 🔑", type="password")
+    login_button = st.sidebar.button("Login 🫢")
+
+    if login_button:
+        if username == CORRECT_USERNAME and password == CORRECT_PASSWORD:
+            st.session_state.authenticated = True
+            st.success("Login successful!")
+            st.experimental_rerun()
+            render_main_program()
+        else:
+            st.error("Invalid username or password. Please try again.")
+
+def features():
+    st.write("***🔑 Key Features of OxSecure Intelligence***")
+    
+    with st.expander("📖 In-Depth Information on Cybersecurity Topics"):
+        st.write("""
+            **Expand Your Cybersecurity Knowledge**  
+            Stay informed with detailed articles and guides covering a wide range of cybersecurity topics. Whether you're 
+            learning basic concepts or exploring advanced techniques, this resource ensures you're well-equipped to handle 
+            the latest cybersecurity challenges.
+        """)
+        
+    with st.expander("💻 Secure Coding Principles"):
+        st.write("""
+            **Write Code that Stands the Test of Time**  
+            Learn essential best practices for writing secure, reliable code. Our secure coding guides offer practical tips 
+            and real-world examples to help you minimize vulnerabilities in your software.
+        """)
+        
+    with st.expander("🚨 Major Cyberattacks"):
+        st.write("""
+            **Stay Informed on Critical Threats**  
+            Keep up-to-date on the most significant cyberattacks around the world. Analyze real-world incidents, learn the 
+            attack vectors used, and discover defensive strategies to protect against similar threats.
+        """)
+        
+    with st.expander("⚙️ Security Misconfiguration"):
+        st.write("""
+            **Configure with Confidence**  
+            Learn how to avoid common misconfigurations that leave systems exposed. This section provides a comprehensive 
+            guide to correctly configuring security settings, protecting your organization from unnecessary risks.
+        """)
+        
+    with st.expander("🔎 VirusTotal File Analysis"):
+        st.write("""
+            **Instant Malware Scanning**  
+            Upload your files to run advanced malware scans via VirusTotal API. Get real-time reports with detailed threat 
+            intelligence and analysis, ensuring your files are secure before you use or share them.
+        """)
+        
+    with st.expander("🔐 Comprehensive File Analysis"):
+        st.write("""
+            **Analyze Multiple File Types**  
+            OxSecure Intelligence allows you to scan PDFs, images, executables, and logs with ease. From extracting metadata 
+            to conducting thorough file content analysis, you'll have all the tools you need to secure your files.
+        """)
+        
+    with st.expander("🤖 Powered by Gemini LLM"):
+        st.write("""
+            **AI-Powered Insights**  
+            Harness the cutting-edge power of Gemini LLM to get instant, accurate answers to your cybersecurity queries. 
+            With AI-driven insights, you can navigate complex data and extract valuable knowledge faster than ever before.
+        """)
+        
+    with st.expander("🖼️ Image Analysis with Imagen"):
+        st.write("""
+            **Visual Intelligence at Your Fingertips**  
+            Upload images for detailed analysis using the Imagen feature. Whether you're assessing a security measure or 
+            scanning for vulnerabilities, this tool ensures you get the most out of every image.
+        """)
+
+def use_app():
+    st.write("***📋 How to Use OxSecure Intelligence***")
+    
+    st.write("""
+     🚀 **OxSecure Intelligence: Use Cases**
+
+OxSecure Intelligence is a comprehensive cybersecurity tool designed to provide in-depth information on various security topics, analyze images, and perform detailed file analysis. The app consists of three powerful tools:
+
+🛡️ **1. OxSecure Chat**
+
+ ***Use Case:***
+**OxSecure Chat** allows users to gain a deep understanding of cybersecurity topics by generating detailed outputs based on the entered topics. This tool is ideal for:
+
+- **Learning and Research:** Enter any cybersecurity topic to receive a thorough explanation, including secure coding principles and major attack vectors.
+- **Training and Development:** Use the detailed outputs to educate teams or individuals about specific security concepts and practices.
+- **Consultation and Advisory:** Provide clients or stakeholders with well-researched and comprehensive information on cybersecurity issues.
+
+**How It Works:**
+1. Enter a security topic related to cybersecurity.
+2. Receive a detailed response including:
+   - **Secure Coding Principles:** Best practices and guidelines.
+   - **Major Attacks:** Common threats and attack methods.
+
+🖼️ **2. OxSecure ImaGen**
+
+ ***Use Case:***
+**OxSecure ImaGen** offers advanced image analysis by allowing users to input prompts and retrieve detailed information about the image. This tool is perfect for:
+
+- **Image Verification:** Analyze images to extract metadata and ensure they are authentic and unaltered.
+- **Content Analysis:** Understand the content and context of images through custom prompts.
+- **Forensic Analysis:** Utilize the tool in digital forensics to scrutinize image details for investigative purposes.
+
+ **How It Works:**
+1. Upload an image.
+2. Enter prompts to specify the desired output.
+3. Receive detailed information and insights based on the image content.
+
+ 📂 **3. File Analysis**
+
+***Use Case:***
+**File Analysis** is designed for thorough examination of files, providing essential metadata, hash information, and integrating with VirusTotal for comprehensive security analysis. This tool is valuable for:
+
+- **File Verification:** Extract metadata and hash information to verify file integrity and authenticity.
+- **Threat Detection:** Communicate with VirusTotal API to assess the file’s security status and identify potential threats.
+- **Visual Analytics:** Obtain graphical representations of file analysis results to visualize threat levels and security metrics.
+
+**How It Works:**
+1. Upload a file.
+2. Extract metadata and hash information.
+3. Integrate with VirusTotal API for detailed security analysis.
+4. View graphical reports and insights about the file.
+
+---
+
+**OxSecure Intelligence** empowers you with detailed insights and robust analysis tools to enhance your cybersecurity practices and ensure data integrity. Explore these tools to stay ahead of potential threats and make informed decisions!
+""")
+
+## Function to load Gemini vision model and get response
+def get_gemini_response(input_prompt, image):
+    Model = genai.GenerativeModel('gemini-1.5-pro')
+    if input_prompt != "":
+        response = Model.generate_content([input_prompt, image])
+    else:
+        response = Model.generate_content(image)
+    return response.text
+
+
+def render_main_program():
+    st.markdown("# 🔒 Unlock the Future of Cybersecurity with OxSecure")
+    st.divider()
+    st.markdown("**Where Knowledge Meets Innovation! 🚀 Dive into Cyber Brilliance with OxSecure** 🤖 🌟")
+    st.markdown("----")
+
+    # Sidebar for navigation
+    app_choice = st.sidebar.radio("Choose App", 
+         ("Features 🤹🏻‍♀️", 
+          "OxSecure Chat 🤖", 
+          "OxSecure ImaGen 🎨", 
+          "File Analysis 📁", 
+          "Help & Uses 💁🏻"))
+    
+    #Main content selector
+    # app_choice = st.selectbox(
+    #     "Choose App",
+    #     ["Features 🤹🏻‍♀️", "OxSecure Chat 🤖", "OxSecure ImaGen 🎨", "File Analysis 📁", "Help & Uses 💁🏻"]
+    # )
+
+    # Render the selected app based on user's choice
+    if app_choice == "OxSecure Chat 🤖":
+        render_gemini_api_app()
+    elif app_choice == "OxSecure ImaGen 🎨":
+        render_gemini_vision_app()
+    elif app_choice == "File Analysis 📁":
+        render_file_analysis_app()
+    elif app_choice == "Features 🤹🏻‍♀️":
+        features()
+    elif app_choice == "Help & Uses 💁🏻":
+        use_app()
+
+def render_gemini_api_app():
+    st.caption("🚀 Empower Tomorrow, 🛡️ Secure Today: Unleash the Power of Cybersecurity Brilliance! 💻✨ 🛡️💬  ")
+    st.markdown("---")
+
+    st.title("OxSecure Intelligence 🧠")
+    st.markdown("-----")
+    input_text = st.text_input("Search your Security Related Topic 🔍")
+
+    # Prompt Templates
+    first_input_prompt = PromptTemplate(
+        input_variables=['Topic'],
+        template=textwrap.dedent("""
+            As an experienced cybersecurity researcher, provide a comprehensive and detailed explanation about {Topic}. Cover the following aspects:
+            1. Introduction and Importance in well informative
+            2. Key Concepts and Terminologies
+            3. Historical Background and Evolution
+            4. Its Architecture and Types
+            5. Current Trends and Best Practices
+            6. Major Threats and Vulnerabilities
+            7. Case Studies and Real-world Examples
+            8. Future Outlook and Predictions
+            
+            Ensure the information is professional, well-structured, key conceptual  and suitable for someone with an advanced understanding and Beginner of cybersecurity.
+        """)
+    )
+
+    # Select the model
+    model = genai.GenerativeModel('gemini-1.5-pro')
+    safety_settings = {
+        HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_DANGEROUS: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_SEXUAL: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_TOXICITY: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmProbability.HIGH
+    }
+
+    # Memory
+    Topic_memory = ConversationBufferMemory(input_key='Topic', memory_key='chat_history')
+    Policy_memory = ConversationBufferMemory(input_key='secure coding', memory_key='chat_history')
+    Practice_memory = ConversationBufferMemory(input_key='Practice', memory_key='description_history')
+
+    ## GEMINI LLMS
+    llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro")
+    chain = LLMChain(
+        llm=llm, prompt=first_input_prompt, verbose=True, output_key='secure coding', memory=Topic_memory)
+    safety_settings = {
+        HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_DANGEROUS: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_SEXUAL: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_TOXICITY: HarmBlockThreshold.BLOCK_NONE,
+        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmProbability.HIGH
+    }
+    # Prompt Templates
+    second_input_prompt = PromptTemplate(
+        input_variables=['secure coding'],
+        template="write best {secure coding} and perfect code snippet for implementing secure coding to this {Topic} in well detailed and descriptive way use code snippets for each point and describe code."
+    )
+
+    chain2 = LLMChain(
+        llm=llm, prompt=second_input_prompt, verbose=True, output_key='Practice', memory=Policy_memory)
+    # Prompt Templates
+    third_input_prompt = PromptTemplate(
+        input_variables=['Practice'],
+        template="Implement major best Cybersecurity {Practice} for this {Topic} that helps better security postures into any business. illustrate Major cyberattack which is done by misconfiguration of {Topic} and give the informative info about the malware which caused this"
+    )
+    chain3 = LLMChain(llm=llm, prompt=third_input_prompt, verbose=True, output_key='description', memory=Practice_memory)
+    parent_chain = SequentialChain(
+        chains=[chain, chain2, chain3], input_variables=['Topic'], output_variables=['secure coding', 'Practice',
+                                                                                     'description'], verbose=True)
+
+    if input_text:
+        with st.spinner('Processing.... ⏳'):
+            st.text(parent_chain({'Topic': input_text}))
+
+        with st.expander('Your Topic'):
+            st.info(Topic_memory.buffer)
+
+        with st.expander('Major Practices'):
+            st.info(Practice_memory.buffer)
+    st.markdown("---")
+    linkedin_url = "https://www.linkedin.com/in/aditya-pandey-896109224"
+    st.markdown("  Created with 🤗💖 By Aditya Pandey   "  f"[  LinkedIn 🔗]({linkedin_url})")
+
+
+def render_gemini_vision_app():
+    st.title('OxSecure ImaGen 🎨')
+    st.markdown("----")
+    input_prompt = st.text_input("Input Prompt: ", key="input")
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+    image = ""
+    submit = False  # Initialize submit variable
+
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image.", use_column_width=True)
+        submit = st.button("Tell me about the image")
+
+    if submit:
+        response = get_gemini_response(input_prompt, image)
+        st.subheader("The Response is")
+        st.write(response)
+
+    st.markdown("---")
+    linkedin_url = "https://www.linkedin.com/in/aditya-pandey-896109224"
+    st.markdown("  Created with 🤗💖 By Aditya Pandey  " f"[  LinkedIn 🔗]({linkedin_url})")
+
+
+def get_file_hash(file):
+    file.seek(0)  # Reset file pointer to the beginning
+    file_hash = hashlib.sha256(file.read()).hexdigest()
+    file.seek(0)  # Reset file pointer to the beginning
+    return file_hash
+
+# Function to analyze the file using VirusTotal
+def virustotal_analysis(file_hash):
+    url = f"https://www.virustotal.com/api/v3/files/{file_hash}"
+    headers = {"x-apikey": VIRUSTOTAL_API_KEY}
+    response = requests.get(url, headers=headers)
+    if response.status_code == 200:
+        return response.json()
+    else:
+        st.error("Error with VirusTotal API request. Please check your API key or the file hash.")
+        return None
+
+# Function to extract metadata from PE files
+def extract_metadata(file):
+    try:
+        pe = PE(data=file.read())
+        metadata = {
+            "Number of Sections": pe.FILE_HEADER.NumberOfSections,
+            "Time Date Stamp": pe.FILE_HEADER.TimeDateStamp,
+            "Characteristics": pe.FILE_HEADER.Characteristics,
+        }
+        return metadata
+    except PEFormatError:
+        st.error("Uploaded file is not a valid PE format.")
+        return None
+
+
+def analyze_log_file(log_content):
+    # Data storage structures for IPs, Domains, Headers, Sessions
+    ip_data = []
+    domain_data = []
+    header_data = []
+    id_data = []
+
+    # Regular expressions for matching
+    ip_regex = re.compile(r'\b(?:[0-9]{1,3}\.){3}[0-9]{1,3}\b')
+    domain_regex = re.compile(r'\b[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}\b')
+    header_regex = re.compile(r'(User-Agent|Content-Type|Authorization):\s*(.*)', re.IGNORECASE)
+    id_regex = re.compile(r'\b(?:SessionID|UserID|ID|id|sessionid|userid)\s*[:=\s]\s*([a-zA-Z0-9-]+)', re.IGNORECASE)
+
+    log_entries = []
+
+    for line in log_content.splitlines():
+        # Match IPs
+        ips = ip_regex.findall(line)
+        if ips:
+            ip_data.extend(ips)
+
+        # Match Domains
+        domains = domain_regex.findall(line)
+        if domains:
+            domain_data.extend(domains)
+
+        # Match Headers
+        headers = header_regex.findall(line)
+        if headers:
+            header_data.extend(headers)
+
+        # Match IDs (Session IDs, User IDs, etc.)
+        ids = id_regex.findall(line)
+        if ids:
+            id_data.extend(ids)
+
+        log_entries.append(line)
+
+    # Convert to DataFrame
+    log_df = pd.DataFrame(log_entries, columns=["Log Entries"])
+
+    # Additional DataFrames for captured data
+    ip_df = pd.DataFrame(ip_data, columns=["IP Addresses"])
+    domain_df = pd.DataFrame(domain_data, columns=["Domains"])
+    header_df = pd.DataFrame(header_data, columns=["Header Name", "Header Value"])
+    id_df = pd.DataFrame(id_data, columns=["IDs"])
+
+    # Summary of findings
+    summary = {
+        "log_dataframe": log_df,
+        "ip_dataframe": ip_df,
+        "domain_dataframe": domain_df,
+        "header_dataframe": header_df,
+        "id_dataframe": id_df
+    }
+
+    return summary
+
+# Function to create charts from VirusTotal results with theme selection
+def create_virus_total_charts(virus_total_results, theme="light"):
+    if not virus_total_results:
+        return None
+    
+    # Extract the data for the charts
+    stats = virus_total_results['data']['attributes']['last_analysis_stats']
+    labels = list(stats.keys())
+    values = list(stats.values())
+    
+    # Convert data to DataFrame for better handling
+    df = pd.DataFrame({'Analysis Types': labels, 'Count': values})
+    
+    # Set the background color theme based on user input
+    if theme == "dark":
+        plt.style.use("dark_background")
+        text_color = 'white'
+    else:
+        plt.style.use("default")
+        text_color = 'black'
+    
+    # Create a container (figure) with 3 rows and 2 columns of charts
+    fig, axs = plt.subplots(3, 2, figsize=(18, 18))  # 3 rows and 2 columns of charts
+    
+    # --- Bar Chart ---
+    sns.barplot(x='Analysis Types', y='Count', data=df, palette="coolwarm", ax=axs[0, 0])
+    axs[0, 0].set_title("VirusTotal Analysis Results (Bar Chart)", fontsize=14, fontweight='bold', color=text_color)
+    axs[0, 0].tick_params(axis='x', rotation=45, labelsize=10, labelcolor=text_color)  # Rotate x-axis labels
+    
+    # Add value labels on the bar chart
+    for p in axs[0, 0].patches:
+        axs[0, 0].annotate(f'{int(p.get_height())}', (p.get_x() + p.get_width() / 2., p.get_height()), 
+                           ha='center', va='baseline', fontsize=10, color=text_color, xytext=(0, 3), 
+                           textcoords='offset points')
+    
+    # --- Horizontal Bar Chart ---
+    sns.barplot(y='Analysis Types', x='Count', data=df, palette="magma", ax=axs[0, 1], orient='h')
+    axs[0, 1].set_title("VirusTotal Analysis Results (Horizontal Bar)", fontsize=14, fontweight='bold', color=text_color)
+    axs[0, 1].tick_params(axis='y', labelsize=10, labelcolor=text_color)
+    
+    # Add value labels on horizontal bar chart
+    for p in axs[0, 1].patches:
+        axs[0, 1].annotate(f'{int(p.get_width())}', (p.get_width(), p.get_y() + p.get_height() / 2), 
+                           ha='center', va='center_baseline', fontsize=10, color=text_color, xytext=(5, 0),
+                           textcoords='offset points')
+    
+    # --- Pie Chart ---
+    wedges, texts, autotexts = axs[1, 0].pie(values, labels=labels, autopct='%1.1f%%', startangle=90,
+                                             colors=sns.color_palette("coolwarm", len(labels)),
+                                             wedgeprops=dict(edgecolor=text_color))
+    
+    # Format the text and labels
+    for text in texts:
+        text.set_fontsize(10)
+        text.set_color(text_color)
+    
+    for autotext in autotexts:
+        autotext.set_color(text_color)
+    
+    axs[1, 0].set_title("VirusTotal Analysis Results (Pie Chart)", fontsize=14, fontweight='bold', color=text_color)
+    axs[1, 0].axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
+
+    # --- Donut Chart ---
+    wedges, texts, autotexts = axs[1, 1].pie(values, labels=labels, autopct='%1.1f%%', startangle=90,
+                                             pctdistance=0.85, colors=sns.color_palette("Set2", len(labels)),
+                                             wedgeprops=dict(width=0.4, edgecolor=text_color))  # Donut chart
+    
+    # Format the text and labels for Donut Chart
+    for text in texts:
+        text.set_fontsize(10)
+        text.set_color(text_color)
+    
+    for autotext in autotexts:
+        autotext.set_color(text_color)
+    
+    axs[1, 1].set_title("VirusTotal Analysis Results (Donut Chart)", fontsize=14, fontweight='bold', color=text_color)
+    axs[1, 1].axis('equal')  # Equal aspect ratio for donut shape
+    
+    # --- Heatmap (Random Example) ---
+    random_data = np.random.rand(len(labels), len(labels))  # Create a dummy heatmap based on the stats
+    sns.heatmap(random_data, annot=True, cmap="Blues", ax=axs[2, 0], cbar_kws={'label': 'Intensity'})
+    axs[2, 0].set_title("Random Heatmap (Dummy)", fontsize=14, fontweight='bold', color=text_color)
+    axs[2, 0].set_xticklabels(labels, rotation=45, color=text_color)
+    axs[2, 0].set_yticklabels(labels, rotation=0, color=text_color)
+
+    # --- Scatter Plot ---
+    sns.scatterplot(x=labels, y=values, hue=values, palette="deep", s=100, ax=axs[2, 1], legend=False)
+    axs[2, 1].set_title("VirusTotal Analysis Results (Scatter Plot)", fontsize=14, fontweight='bold', color=text_color)
+    axs[2, 1].set_xlabel("Analysis Types", fontsize=12, color=text_color)
+    axs[2, 1].set_ylabel("Count", fontsize=12, color=text_color)
+    axs[2, 1].tick_params(axis='x', rotation=45, labelcolor=text_color)
+    axs[2, 1].tick_params(axis='y', labelcolor=text_color)
+    
+    # Adjust layout for better spacing and clarity
+    fig.tight_layout(pad=4.0)
+    
+    # Set background based on theme
+    fig.patch.set_facecolor('black' if theme == "dark" else 'white')
+    
+    return fig
+
+# Function to create detailed tables from JSON data
+def create_detailed_table(data, title):
+    st.write(f"{title}")
+    
+    # Normalize JSON data into a DataFrame
+    df = pd.json_normalize(data)
+    
+    # Debug: Show raw data and DataFrame
+    st.write("Raw Data:", data)
+
+    if df.empty:
+        st.write("No data available.")
+    else:
+        # Apply minimal styling for debugging
+        styled_df = df.style.background_gradient(cmap='viridis') \
+                          .format(na_rep='N/A', precision=2)
+        
+        # Display the styled DataFrame
+        st.dataframe(styled_df)
+
+# Function to display the analysis results on the dashboard
+def display_analysis_results(metadata, virus_total_results, log_analysis=None):
+    st.write("## Analysis Results")
+
+    # Metadata
+    if metadata:
+        st.write("### 📂 PE File Metadata")
+        create_detailed_table(metadata, "PE File Metadata")
+
+    # VirusTotal Results
+    if virus_total_results:
+        st.write("### 🦠 VirusTotal Results")
+        create_detailed_table(virus_total_results['data'], "VirusTotal Results")
+        st.write("#### 📊 VirusTotal Analysis Stats")
+        st.markdown("------")
+        fig = create_virus_total_charts(virus_total_results)
+        if fig:
+            st.pyplot(fig)
+
+    # Log Analysis
+    if log_analysis is not None:
+        st.write("### 📝 Log Analysis")
+        st.markdown("------")
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            st.write("**IP Addresses:**")
+            st.dataframe(log_analysis.get("ip_dataframe"))
+            
+        with col2:
+            st.write("**Domains:**")
+            st.dataframe(log_analysis.get("domain_dataframe"))
+            
+        col3, col4, col5 = st.columns([2, 1, 1])
+        st.markdown("----------")
+        
+        with col3:
+            st.write("**Log Entries:**")
+            st.dataframe(log_analysis.get("log_dataframe"))
+            
+        with col4:
+            st.write("**IDs (Session/User/Generic):**")
+            if not log_analysis.get("id_dataframe").empty:
+                st.dataframe(log_analysis.get("id_dataframe"))
+            else:
+                st.write("No IDs found.")
+                
+                
+        with col5:
+            st.write("**Headers:**")
+            if not log_analysis.get("header_dataframe").empty:
+                st.dataframe(log_analysis.get("header_dataframe"))
+            else:
+                st.write("No headers found.")
+
+def read_file_with_fallback(byte_data):
+    try:
+        # Attempt to read the file with UTF-8 encoding
+        return byte_data.decode("utf-8")
+    except UnicodeDecodeError:
+        # If UTF-8 decoding fails, try to detect encoding
+        byte_stream = io.BytesIO(byte_data)
+        detected_encoding = chardet.detect(byte_data)['encoding']
+        byte_stream.seek(0)  # Reset stream pointer
+        return byte_stream.read().decode(detected_encoding, errors='replace')
+
+
+def render_file_analysis_app():
+    st.title("🔍 File Analysis Dashboard")
+    st.markdown("---")
+    st.image('ui/antivirus.png', width=80, use_column_width='none')
+
+    uploaded_file = st.file_uploader("Upload any file for analysis", type=["exe", "dll", "log", "pdf", "png", "jpg", "jpeg", "gif", "txt", "zip", "rar", "apk"])
+
+    if uploaded_file:
+        file_hash = get_file_hash(uploaded_file)
+        st.write(f"SHA-256 Hash: {file_hash}")
+
+        file_extension = uploaded_file.name.split('.')[-1].lower()
+
+        # Handle different file types
+        if file_extension in ['png', 'jpg', 'jpeg', 'gif']:
+            st.write("### 📄 Image Preview")
+            image = Image.open(uploaded_file)
+            image.thumbnail((512, 512))  # Resize for preview
+            st.image(image, width=240, caption='Uploaded Image', use_column_width=True)
+            # Save uploaded file temporarily
+            file_path = f"./temp/{uploaded_file.name}"
+            os.makedirs(os.path.dirname(file_path), exist_ok=True)
+            with open(file_path, "wb") as f:
+                f.write(uploaded_file.getbuffer())
+
+            try:
+                with open(file_path, "rb") as file:
+                    file_hash = get_file_hash(file)
+                    metadata = extract_metadata(file)
+                    virus_total_results = virustotal_analysis(file_hash)
+
+            finally:
+                # Clean up
+                os.remove(file_path)
+            
+            log_analysis = None
+
+
+        elif file_extension == 'pdf':
+            st.write("### 📄 PDF File")
+            st.write("PDF preview is not supported. Please use other tools to view.")
+            st.download_button(label="Download PDF", data=uploaded_file, file_name=uploaded_file.name)
+            # Save uploaded file temporarily
+            file_path = f"./temp/{uploaded_file.name}"
+            os.makedirs(os.path.dirname(file_path), exist_ok=True)
+            with open(file_path, "wb") as f:
+                f.write(uploaded_file.getbuffer())
+
+            try:
+                with open(file_path, "rb") as file:
+                    file_hash = get_file_hash(file)
+                    metadata = extract_metadata(file)
+                    virus_total_results = virustotal_analysis(file_hash)
+
+            finally:
+                # Clean up
+                os.remove(file_path)
+            
+            log_analysis = None
+
+        elif file_extension in ['txt', 'log']:
+            st.write("### 📝 Log File Content")
+            log_content = read_file_with_fallback(uploaded_file.getvalue())
+            log_analysis = analyze_log_file(log_content)
+            # Save uploaded file temporarily
+            file_path = f"./temp/{uploaded_file.name}"
+            os.makedirs(os.path.dirname(file_path), exist_ok=True)
+            with open(file_path, "wb") as f:
+                f.write(uploaded_file.getbuffer())
+
+            try:
+                with open(file_path, "rb") as file:
+                    file_hash = get_file_hash(file)
+                    metadata = extract_metadata(file)
+                    virus_total_results = virustotal_analysis(file_hash)
+
+            finally:
+                # Clean up
+                os.remove(file_path)
+            
+            log_analysis = analyze_log_file(log_content)
+
+        elif file_extension in ['zip', 'rar']:
+            st.write("### 📦 Compressed File")
+            st.write("Compressed files require further extraction and analysis.")
+            # Save uploaded file temporarily
+            file_path = f"./temp/{uploaded_file.name}"
+            os.makedirs(os.path.dirname(file_path), exist_ok=True)
+            with open(file_path, "wb") as f:
+                f.write(uploaded_file.getbuffer())
+
+            try:
+                with open(file_path, "rb") as file:
+                    file_hash = get_file_hash(file)
+                    metadata = extract_metadata(file)
+                    virus_total_results = virustotal_analysis(file_hash)
+
+            finally:
+                # Clean up
+                os.remove(file_path)
+            
+            log_analysis = None
+
+        elif file_extension in ['apk', 'exe', 'dll']:
+            # Save uploaded file temporarily
+            file_path = f"./temp/{uploaded_file.name}"
+            os.makedirs(os.path.dirname(file_path), exist_ok=True)
+            with open(file_path, "wb") as f:
+                f.write(uploaded_file.getbuffer())
+
+            try:
+                with open(file_path, "rb") as file:
+                    file_hash = get_file_hash(file)
+                    metadata = extract_metadata(file)
+                    virus_total_results = virustotal_analysis(file_hash)
+
+            finally:
+                # Clean up
+                os.remove(file_path)
+            
+            log_analysis = None
+
+        else:
+            st.error("Unsupported file type.")
+            metadata = None
+            virus_total_results = None
+            log_analysis = None
+
+        display_analysis_results(metadata, virus_total_results, log_analysis)
+                
+
+    st.markdown("---")
+    linkedin_url = "https://www.linkedin.com/in/aditya-pandey-896109224"
+    st.markdown("  Created with 🤗💖 By Aditya Pandey  " f"[  LinkedIn 🔗]({linkedin_url})")
+
+
+def main():
+    if 'authenticated' not in st.session_state:
+        st.session_state.authenticated = False
+
+    if st.session_state.authenticated:
+        render_main_program()
+    else:
+        render_login_page()
+
+if __name__ == "__main__":
+    main()

vt.py

@@ -0,0 +1,317 @@
+import streamlit as st
+import requests
+import hashlib
+import pandas as pd
+import json
+import matplotlib.pyplot as plt
+import seaborn as sns
+from io import BytesIO
+from PIL import Image
+from pefile import PE, PEFormatError
+import os
+import re
+
+# VirusTotal API details
+VIRUSTOTAL_API_KEY = 'ed48e6407e0b7975be7d19c797e1217f500183c9ae84d1119af8628ba4c98c3d'
+
+# streamlit framework
+st.set_page_config(
+    page_title="OxThreat",
+    page_icon="🔍",
+    layout="wide"
+)
+
+# Function to calculate the file's SHA-256 hash
+def get_file_hash(file):
+    file.seek(0)  # Reset file pointer to the beginning
+    file_hash = hashlib.sha256(file.read()).hexdigest()
+    file.seek(0)  # Reset file pointer to the beginning
+    return file_hash
+
+# Function to analyze the file using VirusTotal
+def virustotal_analysis(file_hash):
+    url = f"https://www.virustotal.com/api/v3/files/{file_hash}"
+    headers = {"x-apikey": VIRUSTOTAL_API_KEY}
+    response = requests.get(url, headers=headers)
+    if response.status_code == 200:
+        return response.json()
+    else:
+        st.error("Error with VirusTotal API request. Please check your API key or the file hash.")
+        return None
+
+# Function to extract metadata from PE files
+def extract_metadata(file):
+    try:
+        pe = PE(data=file.read())
+        metadata = {
+            "Number of Sections": pe.FILE_HEADER.NumberOfSections,
+            "Time Date Stamp": pe.FILE_HEADER.TimeDateStamp,
+            "Characteristics": pe.FILE_HEADER.Characteristics,
+        }
+        return metadata
+    except PEFormatError:
+        st.error("Uploaded file is not a valid PE format.")
+        return None
+
+# Function to analyze log files
+def analyze_log_file(log_content):
+    errors = re.findall(r'ERROR.*', log_content)
+    return pd.DataFrame(errors, columns=["Errors"])
+
+# Function to create charts from VirusTotal results
+def create_virus_total_charts(virus_total_results):
+    if not virus_total_results:
+        return None
+    
+    stats = virus_total_results['data']['attributes']['last_analysis_stats']
+    labels = list(stats.keys())
+    values = list(stats.values())
+
+    fig, ax = plt.subplots(figsize=(10, 5))
+    sns.barplot(x=labels, y=values, palette="viridis", ax=ax)
+    ax.set_title("VirusTotal Analysis Results", fontsize=16, fontweight='bold')
+    ax.set_xlabel("Analysis Types", fontsize=14)
+    ax.set_ylabel("Count", fontsize=14)
+
+    return fig
+
+# Function to create detailed tables from JSON data
+def create_detailed_table(data, title):
+    st.write(f"### {title}")
+    
+    # Normalize JSON data into a DataFrame
+    df = pd.json_normalize(data)
+    
+    # Debug: Show raw data and DataFrame
+    st.write("Raw Data:", data)
+
+    if df.empty:
+        st.write("No data available.")
+    else:
+        # Apply minimal styling for debugging
+        styled_df = df.style.background_gradient(cmap='viridis') \
+                          .format(na_rep='N/A', precision=2)
+        
+        # Display the styled DataFrame
+        st.dataframe(styled_df)
+
+# Function to display the analysis results on the dashboard
+def display_analysis_results(metadata, virus_total_results, log_analysis=None):
+    st.write("## Analysis Results")
+
+    col1, col2 = st.columns([2, 1])
+
+    # Metadata
+    with col1:
+        if metadata:
+            st.write("### 📂 PE File Metadata")
+            create_detailed_table(metadata, "PE File Metadata")
+
+    # VirusTotal Results
+    with col1:
+        if virus_total_results:
+            st.write("### 🦠 VirusTotal Results")
+            create_detailed_table(virus_total_results['data'], "VirusTotal Results")
+            st.write("#### 📊 VirusTotal Analysis Stats")
+            fig = create_virus_total_charts(virus_total_results)
+            if fig:
+                st.pyplot(fig)
+
+    # Log Analysis
+    with col2:
+        if log_analysis is not None:
+            st.write("### 📝 Log Analysis")
+            st.table(log_analysis)
+
+# Main page of the Streamlit app
+def main_page():
+    st.title("🦠 Malware Analysis Tool")
+    st.markdown("---")
+    st.image('ui/antivirus.png', width=200, use_column_width='always')
+
+    if st.button("Go to File Analysis 🗂️"):
+        st.session_state.page = "file_analysis"
+        st.experimental_rerun()
+
+# File analysis page where the user can upload files for analysis
+def file_analysis_page():
+    st.title("🔍 File Analysis Dashboard")
+    st.markdown("---")
+    st.image('ui/antivirus.png', width=80, use_column_width='none')
+
+    uploaded_file = st.file_uploader("Upload any file for analysis", type=["exe", "dll", "log", "pdf", "png", "jpg", "jpeg", "gif", "txt", "zip", "rar", "apk"])
+
+    if uploaded_file:
+        file_hash = get_file_hash(uploaded_file)
+        st.write(f"SHA-256 Hash: {file_hash}")
+
+        file_extension = uploaded_file.name.split('.')[-1].lower()
+
+        # Handle different file types
+        if file_extension in ['png', 'jpg', 'jpeg', 'gif']:
+            st.write("### 📄 Image Preview")
+            image = Image.open(uploaded_file)
+            image.thumbnail((150, 150))  # Resize for preview
+            st.image(image, caption='Uploaded Image', use_column_width=True)
+            metadata = None
+            virus_total_results = None
+            log_analysis = None
+
+        elif file_extension == 'pdf':
+            st.write("### 📄 PDF File")
+            st.write("PDF preview is not supported. Please use other tools to view.")
+            st.download_button(label="Download PDF", data=uploaded_file, file_name=uploaded_file.name)
+            metadata = None
+            virus_total_results = None
+            log_analysis = None
+
+        elif file_extension in ['txt', 'log']:
+            st.write("### 📝 Log File Content")
+            log_content = uploaded_file.getvalue().decode("utf-8")
+            log_analysis = analyze_log_file(log_content)
+            metadata = None
+            virus_total_results = None
+
+        elif file_extension in ['zip', 'rar']:
+            st.write("### 📦 Compressed File")
+            st.write("Compressed files require further extraction and analysis.")
+            metadata = None
+            virus_total_results = None
+            log_analysis = None
+
+        elif file_extension in ['apk', 'exe', 'dll']:
+            # Save uploaded file temporarily
+            file_path = f"./temp/{uploaded_file.name}"
+            os.makedirs(os.path.dirname(file_path), exist_ok=True)
+            with open(file_path, "wb") as f:
+                f.write(uploaded_file.getbuffer())
+
+            try:
+                with open(file_path, "rb") as file:
+                    file_hash = get_file_hash(file)
+                    metadata = extract_metadata(file)
+                    virus_total_results = virustotal_analysis(file_hash)
+
+            finally:
+                # Clean up
+                os.remove(file_path)
+            
+            log_analysis = None
+
+        else:
+            st.error("Unsupported file type.")
+            metadata = None
+            virus_total_results = None
+            log_analysis = None
+
+        display_analysis_results(metadata, virus_total_results, log_analysis)
+
+# Initialize session state for page navigation
+if 'page' not in st.session_state:
+    st.session_state.page = "main"
+
+# Routing based on page state
+if st.session_state.page == "main":
+    main_page()
+elif st.session_state.page == "file_analysis":
+    file_analysis_page()
+
+
+
+
+
+
+
+def analyze_log_file(log_content):
+    # Data storage structures for IPs, Domains, Headers, Sessions
+    ip_data = []
+    domain_data = []
+    header_data = []
+    session_data = []
+
+    # Regular expressions for matching
+    ip_regex = re.compile(r'\b(?:[0-9]{1,3}\.){3}[0-9]{1,3}\b')
+    domain_regex = re.compile(r'\b[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}\b')
+    header_regex = re.compile(r'(User-Agent|Content-Type|Authorization):\s*(.*)', re.IGNORECASE)
+    session_regex = re.compile(r'SessionID:\s*([a-zA-Z0-9]+)')
+
+    log_entries = []
+
+    for line in log_content.splitlines():
+        # Match IPs
+        ips = ip_regex.findall(line)
+        if ips:
+            ip_data.extend(ips)
+
+        # Match Domains
+        domains = domain_regex.findall(line)
+        if domains:
+            domain_data.extend(domains)
+
+        # Match Headers
+        headers = header_regex.findall(line)
+        if headers:
+            header_data.extend(headers)
+
+        # Match Sessions
+        sessions = session_regex.findall(line)
+        if sessions:
+            session_data.extend(sessions)
+
+        log_entries.append(line)
+
+    # Convert to DataFrame
+    log_df = pd.DataFrame(log_entries, columns=["Log Entries"])
+
+    # Additional DataFrames for captured data
+    ip_df = pd.DataFrame(ip_data, columns=["IP Addresses"])
+    domain_df = pd.DataFrame(domain_data, columns=["Domains"])
+    header_df = pd.DataFrame(header_data, columns=["Header Name", "Header Value"])
+    session_df = pd.DataFrame(session_data, columns=["Session IDs"])
+
+    # Summary of findings
+    summary = {
+        "log_dataframe": log_df,
+        "ip_dataframe": ip_df,
+        "domain_dataframe": domain_df,
+        "header_dataframe": header_df,
+        "session_dataframe": session_df
+    }
+
+    return summary
+
+# Log Analysis Section
+if log_analysis is not None:
+    st.write("### 📝 Log Analysis")
+
+    # First row: IP Addresses and Domains
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.write("**IP Addresses:**")
+        st.dataframe(log_analysis.get("ip_dataframe"))
+
+    with col2:
+        st.write("**Domains:**")
+        st.dataframe(log_analysis.get("domain_dataframe"))
+
+    # Second row: Log Entries, Session IDs, Headers
+    col3, col4, col5 = st.columns([2, 1, 1])
+
+    with col3:
+        st.write("**Log Entries:**")
+        st.dataframe(log_analysis.get("log_dataframe"))
+
+    with col4:
+        st.write("**Session IDs:**")
+        if not log_analysis.get("session_dataframe").empty:
+            st.dataframe(log_analysis.get("session_dataframe"))
+        else:
+            st.write("No session IDs found.")
+
+    with col5:
+        st.write("**Headers:**")
+        if not log_analysis.get("header_dataframe").empty:
+            st.dataframe(log_analysis.get("header_dataframe"))
+        else:
+            st.write("No headers found.")