app.py

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 sentiment analysis pipeline

# 🔑 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()

#### **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")

# Show first 5 rows from MongoDB
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.")

# 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))

#### **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:")

if st.button("Get AI Response & Sentiment"):
    if user_prompt:
        try:
            # Generate AI response
            model = genai.GenerativeModel("gemini-1.5-pro")
            response = model.generate_content(user_prompt)
            ai_response = response.text

            # Analyze sentiment
            sentiment = analyze_sentiment(ai_response)

            # Display results
            st.write("### AI Response:")
            st.write(ai_response)
            st.write(f"**Sentiment Analysis:** {sentiment}")

        except Exception as e:
            st.error(f"❌ Error: {e}")
    else:
        st.warning("⚠️ Please enter a text input.")