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| from flask import Flask, request, jsonify, send_file | |
| import requests | |
| from bs4 import BeautifulSoup | |
| import trafilatura | |
| import json | |
| import os | |
| import tempfile | |
| from io import BytesIO | |
| from gtts import gTTS | |
| from utils import ( | |
| search_news_articles, | |
| extract_article_content, | |
| perform_sentiment_analysis, | |
| extract_topics, | |
| generate_comparative_analysis, | |
| summarize_sentiment | |
| ) | |
| app = Flask(__name__) | |
| def get_company_news(company_name): | |
| """ | |
| Fetch news articles about a specific company | |
| """ | |
| try: | |
| # Search for news articles | |
| articles = search_news_articles(company_name) | |
| if not articles or len(articles) == 0: | |
| return jsonify({"error": "No articles found"}), 404 | |
| # Process articles to extract content | |
| processed_articles = [] | |
| for article in articles[:10]: # Limit to 10 articles | |
| article_data = extract_article_content(article) | |
| if article_data: | |
| processed_articles.append(article_data) | |
| return jsonify({"articles": processed_articles}) | |
| except Exception as e: | |
| return jsonify({"error": str(e)}), 500 | |
| def analyze_content(): | |
| """ | |
| Perform sentiment analysis and comparative analysis on articles | |
| """ | |
| try: | |
| data = request.json | |
| if not data or 'company' not in data or 'articles' not in data: | |
| return jsonify({"error": "Invalid request data"}), 400 | |
| company_name = data['company'] | |
| articles = data['articles'] | |
| if len(articles) == 0: | |
| return jsonify({"error": "No articles provided for analysis"}), 400 | |
| # Perform sentiment analysis on each article | |
| for article in articles: | |
| if 'Summary' in article: | |
| sentiment = perform_sentiment_analysis(article['Summary']) | |
| article['Sentiment'] = sentiment | |
| # Extract topics | |
| article['Topics'] = extract_topics(article['Summary']) | |
| # Generate comparative analysis | |
| comparative_analysis = generate_comparative_analysis(articles) | |
| # Generate final sentiment summary | |
| final_summary = summarize_sentiment(company_name, articles, comparative_analysis) | |
| # Construct response | |
| response = { | |
| "Company": company_name, | |
| "Articles": articles, | |
| "Comparative Sentiment Score": comparative_analysis, | |
| "Final Sentiment Analysis": final_summary | |
| } | |
| return jsonify(response) | |
| except Exception as e: | |
| return jsonify({"error": str(e)}), 500 | |
| def text_to_speech(): | |
| """ | |
| Convert text to speech in multiple languages | |
| Supported languages: Hindi, English, Spanish, French, German, Japanese, Chinese, Russian, Arabic, Italian | |
| """ | |
| try: | |
| data = request.json | |
| if not data or 'text' not in data: | |
| return jsonify({"error": "No text provided"}), 400 | |
| text = data['text'] | |
| # Default to Hindi if no language specified | |
| language = data.get('language', 'hi') | |
| # Map of supported languages | |
| language_map = { | |
| 'hi': 'Hindi', | |
| 'en': 'English', | |
| 'es': 'Spanish', | |
| 'fr': 'French', | |
| 'de': 'German', | |
| 'ja': 'Japanese', | |
| 'zh-CN': 'Chinese', | |
| 'ru': 'Russian', | |
| 'ar': 'Arabic', | |
| 'it': 'Italian' | |
| } | |
| # Validate language | |
| if language not in language_map: | |
| return jsonify({ | |
| "error": f"Unsupported language code: {language}", | |
| "supported_languages": language_map | |
| }), 400 | |
| # Create a temporary file to store the audio | |
| temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.mp3') | |
| # Generate TTS in the specified language | |
| tts = gTTS(text=text, lang=language, slow=False) | |
| tts.save(temp_file.name) | |
| # Send the audio file | |
| return send_file( | |
| temp_file.name, | |
| mimetype='audio/mp3', | |
| as_attachment=True, | |
| download_name=f'speech_{language}.mp3' | |
| ) | |
| except Exception as e: | |
| return jsonify({"error": str(e)}), 500 | |
| if __name__ == '__main__': | |
| app.run(host='0.0.0.0', port=8000, debug=True) | |