change in app.py and backup.py

app.py

@@ -2,6 +2,7 @@ import streamlit as st
 import pandas as pd
 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
 
 #### **1. Ensure Data is Inserted Before Display**
 insert_data_if_empty()
@@ -10,7 +11,7 @@ insert_data_if_empty()
 collection = get_mongo_client()
 
 #### **3. Streamlit App to Display Data**
-st.title("📊 MongoDB Data Viewer")
+st.title("📊 MongoDB Data Viewer with AI Chatbot")
 
 # Show first 5 rows from MongoDB
 st.subheader("First 5 Rows from Database")
@@ -25,3 +26,23 @@ else:
 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")
+
+# Configure Generative AI model (replace `your-api-key` with actual key)
+genai.configure(api_key="your-api-key")  # 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)
+    else:
+        st.warning("⚠️ Please enter a question.")

backup.py

@@ -1,58 +1,27 @@
 import streamlit as st
 import pandas as pd
-import requests
-import io
 from pymongo import MongoClient
-from transformers import pipeline
+from db import insert_data_if_empty, get_mongo_client  # Import functions from db.py
 
-#### **1. MongoDB Connection**
-def get_mongo_client():
-    client = MongoClient("mongodb+srv://groupA:[email protected]/?retryWrites=true&w=majority&appName=SentimentCluster")
-    db = client["sentiment_db"]
-    return db["tweets"]
+#### **1. Ensure Data is Inserted Before Display**
+insert_data_if_empty()
 
+#### **2. MongoDB Connection**
 collection = get_mongo_client()
 
-#### **2. Load Dataset from Hugging Face**
-csv_url = "https://huggingface.co/spaces/sharangrav24/SentimentAnalysis/resolve/main/sentiment140.csv"
-
-try:
-    response = requests.get(csv_url)
-    response.raise_for_status()  # Ensure the request was successful
-    df = pd.read_csv(io.StringIO(response.text), encoding="ISO-8859-1")
-    st.success("Dataset Loaded Successfully!")
-except Exception as e:
-    st.error(f"Error loading dataset: {e}")
-    st.stop()
-
-#### **3. Sentiment Analysis using BERT-ROBERTA**
-st.info("Running Sentiment Analysis...")
-
-sentiment_pipeline = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")
-
-# Function to analyze sentiment
-def analyze_sentiment(text):
-    return sentiment_pipeline(text)[0]['label']
-
-df["sentiment"] = df["text"].apply(analyze_sentiment)
-
-#### **4. Upload Data to MongoDB**
-collection.delete_many({})  # Optional: Clear existing data before inserting
-collection.insert_many(df.to_dict("records"))
-st.success("Data Uploaded to MongoDB!")
-
-#### **5. Build Streamlit Dashboard**
-st.title("📊 Sentiment Analysis Dashboard")
+#### **3. Streamlit App to Display Data**
+st.title("📊 MongoDB Data Viewer")
 
 # Show first 5 rows from MongoDB
 st.subheader("First 5 Rows from Database")
 data = list(collection.find({}, {"_id": 0}).limit(5))
-st.write(pd.DataFrame(data))
-
-# Buttons to display more data
-if st.button("Show Complete Data"):
-    st.write(df)
 
-if st.button("Show MongoDB Data"):
-    data = list(collection.find({}, {"_id": 0}))
+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))