integration

app.py

@@ -4,6 +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 model
 
 # 🔑 Fetch API key from Hugging Face Secrets
 GEMINI_API_KEY = os.getenv("gemini_api")
@@ -20,7 +21,7 @@ insert_data_if_empty()
 collection = get_mongo_client()
 
 #### **3. Streamlit App to Display Data**
-st.title("📊 MongoDB Data Viewer with AI Chatbot")
+st.title("📊 MongoDB Data Viewer with AI Sentiment Chatbot")
 
 # Show first 5 rows from MongoDB
 st.subheader("First 5 Rows from Database")
@@ -36,20 +37,38 @@ if st.button("Show Complete Data"):
     all_data = list(collection.find({}, {"_id": 0}))
     st.write(pd.DataFrame(all_data))
 
-#### **4. GenAI Chatbot Interface**
-st.subheader("🤖 AI Chatbot")
+#### **4. Sentiment Analysis Chatbot**
+st.subheader("🤖 AI Sentiment Analysis Chatbot")
+
+# Load Hugging Face sentiment analysis model (RoBERTa)
+sentiment_pipeline = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")
 
 # User input for chatbot
-user_prompt = st.text_input("Ask AI something:")
+user_prompt = st.text_input("Enter a text for sentiment analysis:")
 
-if st.button("Get AI Response"):
+if st.button("Analyze Sentiment"):
     if user_prompt:
         try:
-            model = genai.GenerativeModel("gemini-1.5-pro")
-            response = model.generate_content(user_prompt)
-            st.write("### AI Response:")
-            st.write(response.text)
+            # Perform sentiment analysis
+            sentiment_result = sentiment_pipeline(user_prompt)[0]
+
+            # Display sentiment results
+            st.write("### Sentiment Analysis Result:")
+            st.write(f"**Sentiment:** {sentiment_result['label']}")
+            st.write(f"**Confidence Score:** {sentiment_result['score']:.4f}")
+
+            # Fetch similar sentiment examples from MongoDB
+            sentiment_label = sentiment_result["label"].lower()
+            matching_texts = list(collection.find({"sentiment": sentiment_label}, {"_id": 0, "text": 1}).limit(3))
+
+            if matching_texts:
+                st.write("### Similar Sentiment Examples from MongoDB:")
+                for item in matching_texts:
+                    st.write(f"- {item['text']}")
+            else:
+                st.write("No similar sentiment examples found in MongoDB.")
+
         except Exception as e:
             st.error(f"❌ Error: {e}")
     else:
-        st.warning("⚠️ Please enter a question.")
+        st.warning("⚠️ Please enter some text.")

backup.py

@@ -1,8 +1,17 @@
 import streamlit as st
 import pandas as pd
+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
-import google.generativeai as genai  # Import Generative AI library
+
+# 🔑 Fetch API key from Hugging Face Secrets
+GEMINI_API_KEY = os.getenv("gemini_api")
+
+if GEMINI_API_KEY:
+    genai.configure(api_key=GEMINI_API_KEY)
+else:
+    st.error("⚠️ Google API key is missing! Set it in Hugging Face Secrets.")
 
 #### **1. Ensure Data is Inserted Before Display**
 insert_data_if_empty()
@@ -30,19 +39,17 @@ if st.button("Show Complete Data"):
 #### **4. GenAI Chatbot Interface**
 st.subheader("🤖 AI Chatbot")
 
-# Configure Generative AI model (replace `your-api-key` with actual key)
-genai.configure(api_key="secrets")  # Set up your API key
-
-# Initialize model
-model = genai.GenerativeModel("gemini-pro")
-
 # User input for chatbot
 user_prompt = st.text_input("Ask AI something:")
 
 if st.button("Get AI Response"):
     if user_prompt:
-        response = model.generate_content(user_prompt)
-        st.write("### AI Response:")
-        st.write(response.text)
+        try:
+            model = genai.GenerativeModel("gemini-1.5-pro")
+            response = model.generate_content(user_prompt)
+            st.write("### AI Response:")
+            st.write(response.text)
+        except Exception as e:
+            st.error(f"❌ Error: {e}")
     else:
         st.warning("⚠️ Please enter a question.")

requirements.txt

@@ -1,7 +1,7 @@
 pandas==1.5.3
 pandasai==1.4.1
 pymongo==4.3.3
-transformers==4.37.0
+transformers==4.49.0
 torch==2.1.0
 streamlit==1.30.0
 sentencepiece==0.1.99