import requests
from bs4 import BeautifulSoup
from transformers import pipeline
import gtts
import io
import os
from tts import TextToSpeechConverter
from datetime import datetime
import xml.etree.ElementTree as ET
from fake_useragent import UserAgent
import locale

news_topics = {
    "Technology": ["tech", "digital", "software", "hardware", "IT"],
    "AI": ["artificial intelligence", "machine learning", "deep learning", "neural network"],
    "Business": ["company", "corporate", "firm", "enterprise", "startup", "market"],
    "Finance": ["finance", "investment", "stock", "economy", "trading", "bank"],
    "Partnership": ["partner", "collaboration", "alliance", "merger", "acquisition"],
    "Social Media": ["social", "platform", "tweet", "facebook", "instagram", "linkedin", "post"],
    "Innovation": ["innovate", "new", "advance", "breakthrough", "disruption"],
    "Outage": ["outage", "downtime", "disrupt", "service interruption"],
    "Launch": ["launch", "release", "introduce", "unveil"],
    "Publicity": ["public", "campaign", "promo", "advertisement"],
    "Privacy": ["privacy", "data", "security", "breach"],
    "Entertainment": ["entertain", "media", "show", "movie", "series"],
    "Leadership": ["promotion", "leader", "executive", "ceo", "chairman", "manager"],
    "Mergers & Acquisitions": ["merger", "acquisition", "buyout", "takeover"]
}

def fetch_news(company, language=None, region=None):
    base_url = "https://news.google.com/rss/search"
    language = language or locale.getdefaultlocale()[0].replace('_', '-').lower() or 'en-US'
    region = region or 'US'
    params = {
        "q": f'"{company}"',
        "hl": language,
        "gl": region,
        "ceid": f"{region}:{language.split('-')[0]}"
    }
    headers = {"User-Agent": UserAgent().random, "Accept": "application/xml"}
    print(f"Fetching news for {company} with URL: {base_url}?{'&'.join(f'{k}={v}' for k, v in params.items())}")
    try:
        response = requests.get(base_url, headers=headers, params=params, timeout=15)
        print(f"Response status for {company}: {response.status_code}")
        response.raise_for_status()
        soup = BeautifulSoup(response.content, features="xml")
        if not soup:
            print("Error: BeautifulSoup returned None. Falling back to ElementTree.")
            return parse_with_elementtree(response.content, company)
        items = soup.find_all("item")[:10]
        if not items:
            print(f"No news items found in the RSS feed for {company} with BeautifulSoup.")
            return parse_with_elementtree(response.content, company)
        print(f"Found {len(items)} items with BeautifulSoup.")
        articles = []
        for item in items:
            title = getattr(item.title, 'text', "No title") if item.title else "No title"
            desc = getattr(item.description, 'text', title) if item.description else title
            link = item.link.next_sibling.strip() if item.link and item.link.next_sibling else "No link"
            raw_date = getattr(item.pubDate, 'text', "Date not available") if item.pubDate else "Date not available"
            try:
                pub_date = datetime.strptime(raw_date, "%a, %d %b %Y %H:%M:%S %Z").strftime("%a, %d %b %Y")
            except ValueError:
                pub_date = "Date not available"
            desc_soup = BeautifulSoup(desc, "html.parser")
            full_text = desc_soup.get_text(separator=" ").strip()
            summary = full_text.replace(title, "").strip()
            summary_words = summary.split()
            source = title.split(" - ")[-1].strip() if " - " in title else "Unknown Source"
            final_summary = " ".join(summary_words[:80]) + f" - {source}" if len(summary_words) > 10 else f"{title} - {source}"
            articles.append({
                "title": title,
                "summary": final_summary,
                "link": link,
                "pub_date": pub_date
            })
        print(f"Successfully fetched {len(articles)} articles for {company} with BeautifulSoup")
        return articles
    except requests.exceptions.RequestException as e:
        print(f"Request failed for {company}: {str(e)}")
        return []
    except Exception as e:
        print(f"Error processing news for {company} with BeautifulSoup: {str(e)}. Falling back to ElementTree.")
        return parse_with_elementtree(response.content, company)

def parse_with_elementtree(content, company):
    print("Attempting to parse with ElementTree...")
    try:
        root = ET.fromstring(content)
        items = root.findall(".//item")[:10]
        if not items:
            print(f"No news items found in the RSS feed for {company} with ElementTree")
            return []
        articles = []
        for item in items:
            title = item.find("title").text if item.find("title") is not None else "No title"
            desc = item.find("description").text if item.find("description") is not None else title
            link = item.find("link").text if item.find("link") is not None else "No link"
            raw_date = item.find("pubDate").text if item.find("pubDate") is not None else "Date not available"
            try:
                pub_date = datetime.strptime(raw_date, "%a, %d %b %Y %H:%M:%S %Z").strftime("%a, %d %b %Y")
            except ValueError:
                pub_date = "Date not available"
            desc_soup = BeautifulSoup(desc, "html.parser")
            full_text = desc_soup.get_text(separator=" ").strip()
            summary = full_text if full_text else title
            summary_words = summary.split()
            source = title.split(" - ")[-1].strip() if " - " in title else "Unknown Source"
            final_summary = " ".join(summary_words[:80]) + f" - {source}" if len(summary_words) > 10 else f"{title} - {source}"
            articles.append({
                "title": title,
                "summary": final_summary,
                "link": link,
                "pub_date": pub_date
            })
        print(f"Successfully fetched {len(articles)} articles for {company} with ElementTree")
        return articles
    except Exception as e:
        print(f"Error processing news for {company} with ElementTree: {str(e)}")
        return []

sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")

def analyze_sentiment(text):
    try:
        result = sentiment_analyzer(text[:512])[0]
        score = result["score"]
        label = result["label"]
        if score < 0.7 or "how to" in text.lower() or "review" in text.lower():
            return "Neutral"
        return "Positive" if label == "POSITIVE" else "Negative"
    except Exception as e:
        print(f"Sentiment analysis error: {e}")
        return "Neutral"

def extract_topics(text, max_topics=2):
    text_lower = text.lower()
    topic_scores = {}
    for topic, keywords in news_topics.items():
        count = sum(text_lower.count(keyword.lower()) for keyword in keywords)
        if count > 0:
            topic_scores[topic] = count
    sorted_topics = sorted(topic_scores.items(), key=lambda x: x[1], reverse=True)
    return [topic for topic, _ in sorted_topics][:max_topics] if sorted_topics else ["General News"]

tts_converter = TextToSpeechConverter()

def generate_tts(text, language='hi'):
    try:
        if language == 'hi':
            result = tts_converter.generate_speech(text)
            if result["success"]:
                print(f"Hindi audio generated in memory")
                return result["audio_buffer"]
            else:
                print(f"Hindi audio error: {result['message']}")
                return None
        else:
            tts = gtts.gTTS(text=text, lang='en', slow=False)
            audio_buffer = io.BytesIO()
            tts.write_to_fp(audio_buffer)
            audio_buffer.seek(0)
            return audio_buffer
    except Exception as e:
        print(f"Audio generation error for {language}: {str(e)}")
        return None