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.")