import os
import re
import streamlit as st
import google.generativeai as genai
from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
from db import get_entry_by_index  # For fetching a specific entry from MongoDB

# Configure Gemini API key
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 pre-trained sentiment analysis model
MODEL_NAME = "cardiffnlp/twitter-roberta-base-sentiment"
try:
    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
    model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
    sentiment_pipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
except Exception as e:
    st.error(f"❌ Error loading sentiment model: {e}")

# Load Topic Extraction Model
try:
    topic_pipeline = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
except Exception as e:
    st.error(f"❌ Error loading topic extraction model: {e}")

# Predefined topic labels for classification
TOPIC_LABELS = [
    "Technology", "Politics", "Business", "Sports", "Entertainment",
    "Health", "Science", "Education", "Finance", "Travel", "Food"
]

def analyze_sentiment(text):
    try:
        result = sentiment_pipeline(text)[0]
        label = result['label']
        score = result['score']
        sentiment_mapping = {
            "LABEL_0": "Negative",
            "LABEL_1": "Neutral",
            "LABEL_2": "Positive"
        }
        return sentiment_mapping.get(label, "Unknown"), score
    except Exception as e:
        return f"Error analyzing sentiment: {e}", None

def extract_topic(text):
    try:
        result = topic_pipeline(text, TOPIC_LABELS)
        top_topic = result["labels"][0]
        confidence = result["scores"][0]
        return top_topic, confidence
    except Exception as e:
        return f"Error extracting topic: {e}", None

# Helper: extract an entry index from a query string.
# For example, "data entry 1" or "entry 2" will return index 0 or 1 respectively.
def extract_entry_index(prompt):
    match = re.search(r'(data entry|entry)\s+(\d+)', prompt, re.IGNORECASE)
    if match:
        index = int(match.group(2)) - 1  # Convert to 0-based index
        return index
    return None

def chatbot_response(user_prompt):
    if not user_prompt:
        return None, None, None, None, None

    try:
        # Check if the user query asks for a specific dataset entry.
        entry_index = extract_entry_index(user_prompt)
        if entry_index is not None:
            # Fetch the requested entry from MongoDB.
            entry = get_entry_by_index(entry_index)
            if entry is None:
                return "❌ No entry found for the requested index.", None, None, None, None
            # Extract the required fields.
            entry_text = entry.get("text", "No text available.")
            entry_user = entry.get("user", "Unknown")
            entry_date = entry.get("date", "Unknown")
            
            # Build a static response message with only the desired parts.
            ai_response = (
                "Let's break down this tweet-like MongoDB entry:\n\n"
                f"Text: {entry_text}\n"
                f"User: {entry_user}\n"
                f"Date: {entry_date}"
            )
            
            # Perform sentiment and topic analysis on the entry's text.
            sentiment_label, sentiment_confidence = analyze_sentiment(entry_text)
            topic_label, topic_confidence = extract_topic(entry_text)
            
            return ai_response, sentiment_label, sentiment_confidence, topic_label, topic_confidence
        else:
            # For other queries, use the generative model flow.
            model_gen = genai.GenerativeModel("gemini-1.5-pro")
            ai_response_obj = model_gen.generate_content(user_prompt)
            ai_response = ai_response_obj.text
            
            # Perform sentiment and topic analysis on the user prompt.
            sentiment_label, sentiment_confidence = analyze_sentiment(user_prompt)
            topic_label, topic_confidence = extract_topic(user_prompt)
            
            return ai_response, sentiment_label, sentiment_confidence, topic_label, topic_confidence
    except Exception as e:
        return f"❌ Error: {e}", None, None, None, None