chatbot.py push

app.py

@@ -1,18 +1,7 @@
 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
-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")
-
-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.")
+from db import insert_data_if_empty, get_mongo_client
+from chatbot import chatbot_response  # Import chatbot function
 
 #### **1. Ensure Data is Inserted Before Display**
 insert_data_if_empty()
@@ -24,60 +13,33 @@ collection = get_mongo_client()
 st.title("📊 MongoDB Data Viewer with AI Sentiment Chatbot")
 
 # Show first 5 rows from MongoDB
-#st.subheader("First 5 Rows from Database")
-#data = list(collection.find({}, {"_id": 0}).limit(5))
+st.subheader("First 5 Rows from Database")
+data = list(collection.find({}, {"_id": 0}).limit(5))
 
-#if data:
-#    st.write(pd.DataFrame(data))
-#else:
-#    st.warning("⚠️ No data found. Try refreshing the app.")
+if data:
+    st.write(pd.DataFrame(data))
+else:
+    st.warning("⚠️ No data found. Try refreshing the app.")
 
 # Button to show full MongoDB data
-#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")
+if st.button("Show Complete Data"):
+    all_data = list(collection.find({}, {"_id": 0}))
+    st.write(pd.DataFrame(all_data))
 
-# 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**
+#### **4. AI Chatbot with Sentiment Analysis**
 st.subheader("🤖 AI Chatbot with Sentiment Analysis")
 
 # User input for chatbot
 user_prompt = st.text_area("Ask AI something or paste text for sentiment analysis:")
 
 if st.button("Analyze Sentiment & Get AI Response"):
-    if user_prompt:
-        try:
-            # AI Response from Gemini
-            model = genai.GenerativeModel("gemini-1.5-pro")
-            ai_response = model.generate_content(user_prompt)
-
-            # Sentiment Analysis
-            sentiment_label, confidence = analyze_sentiment(user_prompt)
-
-            # Display AI Response & Sentiment Analysis
-            st.write("### AI Response:")
-            st.write(ai_response.text)
+    ai_response, sentiment_label, confidence = chatbot_response(user_prompt)
 
-            st.write("### Sentiment Analysis:")
-            st.write(f"**Sentiment:** {sentiment_label} ({confidence:.2f} confidence)")
+    if ai_response:
+        st.write("### AI Response:")
+        st.write(ai_response)
 
-        except Exception as e:
-            st.error(f"❌ Error: {e}")
+        st.write("### Sentiment Analysis:")
+        st.write(f"**Sentiment:** {sentiment_label} ({confidence:.2f} confidence)")
     else:
         st.warning("⚠️ Please enter a question or text for sentiment analysis.")

backup.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 Hugging Face transformers for sentiment analysis
 
 # 🔑 Fetch API key from Hugging Face Secrets
 GEMINI_API_KEY = os.getenv("gemini_api")
@@ -20,36 +21,64 @@ 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")
-data = list(collection.find({}, {"_id": 0}).limit(5))
+#st.subheader("First 5 Rows from Database")
+#data = list(collection.find({}, {"_id": 0}).limit(5))
 
-if data:
-    st.write(pd.DataFrame(data))
-else:
-    st.warning("⚠️ No data found. Try refreshing the app.")
+#if data:
+#    st.write(pd.DataFrame(data))
+#else:
+#    st.warning("⚠️ No data found. Try refreshing the app.")
 
 # Button to show full MongoDB data
-if st.button("Show Complete Data"):
-    all_data = list(collection.find({}, {"_id": 0}))
-    st.write(pd.DataFrame(all_data))
+#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
 
-#### **4. GenAI Chatbot Interface**
-st.subheader("🤖 AI Chatbot")
+    # 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("Ask AI something:")
+user_prompt = st.text_area("Ask AI something or paste text for sentiment analysis:")
 
-if st.button("Get AI Response"):
+if st.button("Analyze Sentiment & Get AI Response"):
     if user_prompt:
         try:
+            # AI Response from Gemini
             model = genai.GenerativeModel("gemini-1.5-pro")
-            response = model.generate_content(user_prompt)
+            ai_response = model.generate_content(user_prompt)
+
+            # Sentiment Analysis
+            sentiment_label, confidence = analyze_sentiment(user_prompt)
+
+            # Display AI Response & Sentiment Analysis
             st.write("### AI Response:")
-            st.write(response.text)
+            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 question.")
+        st.warning("⚠️ Please enter a question or text for sentiment analysis.")
+

chatbot.py

@@ -0,0 +1,46 @@
+import streamlit as st
+import google.generativeai as genai
+from transformers import pipeline
+import os
+
+# 🔑 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.")
+
+# 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
+
+# Function to generate AI response & analyze sentiment
+def chatbot_response(user_prompt):
+    if not user_prompt:
+        return None, None, None
+
+    try:
+        # AI Response from Gemini
+        model = genai.GenerativeModel("gemini-1.5-pro")
+        ai_response = model.generate_content(user_prompt)
+
+        # Sentiment Analysis
+        sentiment_label, confidence = analyze_sentiment(user_prompt)
+
+        return ai_response.text, sentiment_label, confidence
+    except Exception as e:
+        return f"❌ Error: {e}", None, None

db.py

@@ -28,6 +28,3 @@ def insert_data_if_empty():
             print("✅ Data Inserted into MongoDB!")
         except Exception as e:
             print(f"❌ Error loading dataset: {e}")
-
-# Uncomment the below line if running `db.py` independently
-# insert_data_if_empty()