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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 model
# 🔑 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. 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")
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))
#### **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("Enter a text for sentiment analysis:")
if st.button("Analyze Sentiment"):
if user_prompt:
try:
# 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 some text.")
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