# app_news_sentiment_firebase.py
import feedparser
import pandas as pd
import gradio as gr
from transformers import pipeline
from datetime import datetime
import firebase_admin
from firebase_admin import credentials, db
import os
import json

# --- Firebase Configuration ---
# IMPORTANT: Set these environment variables in your deployment environment (e.g., Hugging Face Spaces Secrets)
# 1. FIRESTORE_SA_KEY: The full JSON content of your Firebase service account key.
# 2. FIREBASE_DB_URL: The URL to your Firebase Realtime Database.
SA_KEY_JSON = os.environ.get('FIRESTORE_SA_KEY')
DB_URL = os.environ.get('FIREBASE_DB_URL')

class NewsRTDBLogger:
    def __init__(self, db_ref_name='news_sentiment'):
        self.ref = None
        if not all([SA_KEY_JSON, DB_URL]):
            print("NEWS LOGGER: Firebase secrets not set. Logger is disabled.")
            return
        try:
            # The service account key is loaded from a JSON string in the environment variable
            cred_dict = json.loads(SA_KEY_JSON)
            cred = credentials.Certificate(cred_dict)
            if not firebase_admin._apps:
                firebase_admin.initialize_app(cred, {'databaseURL': DB_URL})
            self.ref = db.reference(db_ref_name)
            print(f"NEWS LOGGER: Successfully connected to Firebase RTDB at '{db_ref_name}'.")
        except Exception as e:
            print(f"NEWS LOGGER: CRITICAL ERROR - Failed to initialize: {e}")

    def log_sentiment(self, pub_date, headline, sentiment, confidence):
        if not self.ref:
            return
        try:
            # Create a unique key based on the timestamp to keep logs ordered
            log_entry = {
                "timestamp_published": pub_date,
                "headline": headline,
                "sentiment": sentiment,
                "confidence_score": float(confidence)
            }
            self.ref.push(log_entry)
            # print(f"NEWS LOGGER: Logged '{headline}'")
        except Exception as e:
            print(f"NEWS LOGGER: CRITICAL ERROR - Could not write sentiment log: {e}")

# --- Initialize Global Objects ---
sentiment_pipeline = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-sentiment-latest")
news_logger = NewsRTDBLogger() # Initialize the logger

# --- Core Functions ---
def fetch_news():
    """Fetches the latest news headlines from the FXStreet RSS feed."""
    url = "https://www.fxstreet.com/rss/news"
    feed = feedparser.parse(url)
    # Fetch a few more headlines to ensure we get recent, unique ones
    headlines = feed.entries[:15]
    return headlines

def analyze_news():
    """
    Analyzes news sentiment, logs it to Firebase, and returns a DataFrame for display.
    """
    headlines = fetch_news()
    results = []
    
    for h in headlines:
        text = h.title
        # Get the publication date, fallback to now if not present
        pub_date = h.get("published", datetime.utcnow().isoformat())
        
        # Perform sentiment analysis
        pred = sentiment_pipeline(text)[0]
        label = pred["label"].lower()
        score = round(pred["score"], 3)
        
        if label == "positive":
            market_sentiment = "Bullish"
        elif label == "negative":
            market_sentiment = "Bearish"
        else:
            market_sentiment = "Neutral"
        
        # Log the result to Firebase
        news_logger.log_sentiment(pub_date, text, market_sentiment, score)
        
        results.append([pub_date, text, market_sentiment, score])
        
    # Create DataFrame for Gradio output, showing the 7 most recent
    df = pd.DataFrame(results, columns=["Date", "Headline", "Market Sentiment", "Confidence"])
    return df.head(7)

# --- Gradio Interface ---
demo = gr.Interface(
    fn=analyze_news,
    inputs=None,
    outputs=gr.Dataframe(headers=["Date", "Headline", "Market Sentiment", "Confidence"]),
    title="📊 Forex Sentiment Agent (Firebase Edition)",
    description="Live Forex headlines sentiment (Bullish / Bearish / Neutral). Now logging results directly to Firebase.",
    allow_flagging='never'
)

if __name__ == "__main__":
    demo.launch()