Update app.py

app.py

@@ -1,32 +1,57 @@
 import streamlit as st
-import requests
-
-API_URL = "http://127.0.0.1:8000/analyze/"
-
-st.set_page_config(page_title="Sentiment + Emotion + Toxicity Analyzer", layout="centered")
-
-st.title("🧠 Text Analyzer")
-st.write("Analyze text for **Sentiment**, **Emotions**, and **Toxicity** with refinement.")
+from transformers import pipeline
+
+st.set_page_config(page_title="Sentiment & Emotion Analyzer", layout="centered")
+st.title("🧠 Text Analyzer (Streamlit)")
+
+# Load models
+sentiment_pipeline = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+emotion_pipeline = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion")
+toxicity_pipeline = pipeline("text-classification", model="martin-ha/toxic-comment-model")
+
+# Refinement function
+def refine_predictions(sentiment, sentiment_conf, emotions, toxicity):
+    if emotions.get("fear", 0) > 0.8 or emotions.get("anger", 0) > 0.8 or emotions.get("sadness", 0) > 0.8:
+        sentiment = "negative"
+    if emotions.get("joy", 0) > 0.7:
+        sentiment = "positive"
+    max_emotion = max(emotions, key=emotions.get)
+    if sentiment == "neutral" and emotions[max_emotion] > 0.7:
+        if max_emotion == "joy":
+            sentiment = "positive"
+        else:
+            sentiment = "negative"
+    return sentiment
 
-user_input = st.text_area("Enter your text here:", height=150)
+# UI
+user_input = st.text_area("Enter text here:")
 
 if st.button("Analyze"):
-    if user_input.strip():
-        response = requests.post(API_URL, json={"text": user_input})
-        if response.status_code == 200:
-            result = response.json()
-
-            st.subheader("📊 Results")
-            st.write(f"**Sentiment:** {result['sentiment']} (confidence: {result['sentiment_confidence']:.2f})")
-
-            st.write("### Emotions")
-            for emo, score in result["emotions"].items():
-                st.write(f"- {emo}: {score:.2f}")
-
-            st.write("### Toxicity")
-            for tox, score in result["toxicity"].items():
-                st.write(f"- {tox}: {score:.2f}")
-        else:
-            st.error("Error: Could not analyze text.")
+    if user_input.strip() != "":
+        # Sentiment
+        sent_res = sentiment_pipeline(user_input)[0]
+        sentiment = sent_res["label"].lower()
+        sentiment_conf = sent_res["score"]
+
+        # Emotions
+        emo_res = emotion_pipeline(user_input)[0]
+        emotions = {e["label"].lower(): e["score"] for e in emo_res}
+
+        # Toxicity
+        tox_res = toxicity_pipeline(user_input)[0]
+        toxicity = {t["label"].lower(): t["score"] for t in tox_res}
+
+        # Refine sentiment based on emotion
+        refined_sentiment = refine_predictions(sentiment, sentiment_conf, emotions, toxicity)
+
+        # Display results
+        st.subheader("📊 Results")
+        st.write(f"**Sentiment:** {refined_sentiment} (confidence: {sentiment_conf:.2f})")
+        st.write("### Emotions")
+        for emo, score in emotions.items():
+            st.write(f"- {emo}: {score:.2f}")
+        st.write("### Toxicity")
+        for tox, score in toxicity.items():
+            st.write(f"- {tox}: {score:.2f}")
     else:
         st.warning("Please enter some text to analyze.")