Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,116 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+from io import BytesIO
+import json
+from gpt4all import GPT4All
+from transformers import pipeline
 
-"""
-# Welcome to Streamlit!
+# Page setup
+st.set_page_config(page_title="Top Task Prioritizer with AI", layout="wide")
+st.title("📋 Top 3 Task Prioritizer + AI JSON Analysis")
+
+# Priority mapping
+priority_map = {"High": 3, "Medium": 2, "Low": 1, "N/A": 0}
+
+# Load GPT4All model
+@st.cache_resource
+def load_gpt4all_model():
+    model_path = "C:\\Users\\puttpav\\OneDrive - acuitykp\\Joshua Cyril's files - AI Team\\models"
+    return GPT4All(
+        model_name="Nous-Hermes-2-Mistral-7B-DPO.Q4_0.gguf",
+        allow_download=False,
+        device="cpu",
+        model_path=model_path,
+    )
+
+# Load Qwen model
+@st.cache_resource
+def load_qwen_model():
+    return pipeline("text-generation", model="Qwen/Qwen3-4B-Instruct-2507")
+
+# Load models
+gpt4all_model = load_gpt4all_model()
+qwen_pipe = load_qwen_model()
+
+# Suggest action based on priority and effort
+def suggest_action(row):
+    if row["Priority Score"] == 3 and row["Time Required (hrs)"] <= 20:
+        return "✅ Do First"
+    elif row["Priority Score"] >= 2 and row["Time Required (hrs)"] <= 30:
+        return "📅 Schedule"
+    elif row["Priority Score"] >= 1:
+        return "📤 Delegate"
+    else:
+        return "🕓 Defer"
+
+# File upload
+uploaded_file = st.file_uploader("📂 Upload your Excel file", type=["xlsx"])
+
+if uploaded_file:
+    df = pd.read_excel(uploaded_file)
 
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+    required_columns = ["Date", "Task", "Priority", "Time Required (hrs)"]
+    if all(col in df.columns for col in required_columns):
+        df["Priority Score"] = df["Priority"].map(priority_map)
+        df["Final Score"] = df["Priority Score"] * 10 - df["Time Required (hrs)"]
+        df["Suggested Action"] = df.apply(suggest_action, axis=1)
+        df["Date"] = df["Date"].astype(str)
 
-In the meantime, below is an example of what you can do with just a few lines of code:
+        task_json = df[["Date", "Task", "Priority", "Time Required (hrs)", "Suggested Action"]].to_dict(orient="records")
+
+        st.success("✅ Tasks processed and ready for AI analysis!")
+        st.dataframe(df, use_container_width=True)
+
+        # Prompt for AI
+        prompt = f"""
+You are a task prioritization assistant. You will be given a list of tasks in JSON format. Your job is to select the top 3 most important tasks ONLY from the provided list based on their priority and time required.
+
+Do NOT invent or add any new tasks. Only choose from the tasks listed below and then show the recommendation for each task.
+
+Respond ONLY with a valid JSON array of EXACTLY 3 tasks. Each object should include:
+- Task Name
+- Priority Level
+- Time Required
+- Suggested Action
+- Recommendation (e.g., break into subtasks, delegate, schedule early)
+
+Tasks:
+{json.dumps(task_json, indent=2)}
 """
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+        # Model selection
+        model_choice = st.selectbox("🤖 Choose AI Model", ["GPT4All", "Qwen"])
+
+        if st.button("🔍 Analyze with Selected AI"):
+            with st.spinner(f"Analyzing with {model_choice}..."):
+                if model_choice == "GPT4All":
+                    response_text = ""
+                    with gpt4all_model.chat_session():
+                        for token in gpt4all_model.generate(prompt, max_tokens=1024):
+                            response_text += token
+                else:
+                    response_text = qwen_pipe(prompt, max_new_tokens=1024)[0]["generated_text"]
+
+                try:
+                    json_start = response_text.find("[")
+                    json_end = response_text.rfind("]") + 1
+                    json_str = response_text[json_start:json_end]
+                    parsed_json = json.loads(json_str)
+
+                    result_df = pd.DataFrame(parsed_json)
+                    st.success(f"✅ Top 3 tasks selected by {model_choice}")
+                    st.dataframe(result_df, use_container_width=True)
+
+                    output = BytesIO()
+                    result_df.to_excel(output, index=False)
+                    st.download_button(
+                        label="📥 Download Top 3 Tasks (AI Selected)",
+                        data=output.getvalue(),
+                        file_name=f"top_prioritized_tasks_by_{model_choice.lower()}.xlsx",
+                        mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+                    )
+                except Exception as e:
+                    st.error(f"❌ Failed to parse JSON from {model_choice} response.")
+                    st.text_area("Raw AI Output", value=response_text, height=700)
+    else:
+        st.error(f"Missing required columns. Please include: {', '.join(required_columns)}")