changed the sentiment logic

app.py

@@ -4,7 +4,7 @@ import google.generativeai as genai  # Import Generative AI library
 import os
 from pymongo import MongoClient
 from db import insert_data_if_empty, get_mongo_client  # Import functions from db.py
-from transformers import pipeline  # Import sentiment analysis pipeline
+from transformers import pipeline  # Import Hugging Face transformers for sentiment analysis
 
 # 🔑 Fetch API key from Hugging Face Secrets
 GEMINI_API_KEY = os.getenv("gemini_api")
@@ -20,16 +20,8 @@ insert_data_if_empty()
 #### **2. MongoDB Connection**
 collection = get_mongo_client()
 
-#### **3. Load Sentiment Analysis Model**
-sentiment_pipeline = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")
-
-def analyze_sentiment(text):
-    """Analyze sentiment using RoBERTa model."""
-    sentiment_result = sentiment_pipeline(text)[0]['label']
-    return sentiment_result  # Returns "LABEL_0", "LABEL_1", or "LABEL_2"
-
-#### **4. Streamlit App to Display Data**
-st.title("📊 MongoDB Data Viewer with AI & Sentiment Analysis")
+#### **3. Streamlit App to Display Data**
+st.title("📊 MongoDB Data Viewer with AI Sentiment Chatbot")
 
 # Show first 5 rows from MongoDB
 st.subheader("First 5 Rows from Database")
@@ -45,29 +37,47 @@ if st.button("Show Complete Data"):
     all_data = list(collection.find({}, {"_id": 0}))
     st.write(pd.DataFrame(all_data))
 
+#### **4. Load Sentiment Analysis Model (RoBERTa)**
+sentiment_pipeline = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")
+
+# Function to analyze sentiment
+def analyze_sentiment(text):
+    sentiment_result = sentiment_pipeline(text)[0]
+    label = sentiment_result['label']  # Extract sentiment label (POSITIVE, NEGATIVE, NEUTRAL)
+    score = sentiment_result['score']  # Extract confidence score
+
+    # Convert labels to a readable format
+    sentiment_mapping = {
+        "LABEL_0": "Negative",
+        "LABEL_1": "Neutral",
+        "LABEL_2": "Positive"
+    }
+    return sentiment_mapping.get(label, "Unknown"), score
+
 #### **5. AI Chatbot with Sentiment Analysis**
 st.subheader("🤖 AI Chatbot with Sentiment Analysis")
 
 # User input for chatbot
-user_prompt = st.text_input("Paste text here for AI sentiment analysis:")
+user_prompt = st.text_area("Ask AI something or paste text for sentiment analysis:")
 
-if st.button("Get AI Response & Sentiment"):
+if st.button("Analyze Sentiment & Get AI Response"):
     if user_prompt:
         try:
-            # Generate AI response
+            # AI Response from Gemini
             model = genai.GenerativeModel("gemini-1.5-pro")
-            response = model.generate_content(user_prompt)
-            ai_response = response.text
+            ai_response = model.generate_content(user_prompt)
 
-            # Analyze sentiment
-            sentiment = analyze_sentiment(ai_response)
+            # Sentiment Analysis
+            sentiment_label, confidence = analyze_sentiment(user_prompt)
 
-            # Display results
+            # Display AI Response & Sentiment Analysis
             st.write("### AI Response:")
-            st.write(ai_response)
-            st.write(f"**Sentiment Analysis:** {sentiment}")
+            st.write(ai_response.text)
+
+            st.write("### Sentiment Analysis:")
+            st.write(f"**Sentiment:** {sentiment_label} ({confidence:.2f} confidence)")
 
         except Exception as e:
             st.error(f"❌ Error: {e}")
     else:
-        st.warning("⚠️ Please enter a text input.")
+        st.warning("⚠️ Please enter a question or text for sentiment analysis.")