chatbot.py

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, get_dataset_summary

# 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 entry index from prompt (e.g., "data entry 1" yields index 0)
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

# Helper: Detect if the query is asking for a specific dataset entry.
def is_entry_query(prompt):
    index = extract_entry_index(prompt)
    if index is not None:
        return True, index
    return False, None

# Helper: Detect if the query is a basic dataset question.
# Examples: "What is the dataset summary?", "Show me the sentiment distribution", etc.
def is_basic_dataset_question(prompt):
    lower = prompt.lower()
    keywords = ["dataset summary", "total tweets", "sentiment distribution", "overall dataset", "data overview", "data summary"]
    return any(keyword in lower for keyword in keywords)

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

    try:
        # If the query is a basic dataset question, fetch summary from MongoDB.
        if is_basic_dataset_question(user_prompt):
            summary = get_dataset_summary()
            ai_response = "Dataset Summary:\n" + summary
            # Run analysis on the summary text
            sentiment_label, sentiment_confidence = analyze_sentiment(summary)
            topic_label, topic_confidence = extract_topic(summary)
            return ai_response, sentiment_label, sentiment_confidence, topic_label, topic_confidence

        # If the query is about a specific entry in the dataset...
        entry_query, index = is_entry_query(user_prompt)
        if entry_query:
            entry = get_entry_by_index(index)
            if entry is None:
                return "❌ No entry found for the requested index.", None, None, None, None
            # Retrieve fields from the document
            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 the required format
            ai_response = (
                "Let's break down this tweet-like MongoDB entry:\n\n"
                f"Tweet: {entry_text}\n"
                f"User: {entry_user}\n"
                f"Date: {entry_date}"
            )
            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

        # For other queries, use the generative model (this branch may be slower).
        model_gen = genai.GenerativeModel("gemini-1.5-pro")
        ai_response_obj = model_gen.generate_content(user_prompt)
        ai_response = ai_response_obj.text
        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