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| from fastapi import FastAPI | |
| from pydantic import BaseModel | |
| from utils import fetch_news, analyze_sentiment, extract_topics, generate_tts | |
| import random | |
| # Set up the FastAPI server with a name and description | |
| app = FastAPI(title="News Sentiment API", description="Analyze news sentiment for companies") | |
| class CompanyInput(BaseModel): | |
| """Simple model to validate incoming company name.""" | |
| company: str | |
| async def analyze_company(input: CompanyInput): | |
| """Take a company name and return its news sentiment analysis.""" | |
| company = input.company | |
| articles_data = fetch_news(company) | |
| if not articles_data: | |
| return {"error": f"No articles found for {company}. Check logs for details."} | |
| articles = [] | |
| sentiments = {"Positive": 0, "Negative": 0, "Neutral": 0} | |
| positive_articles = [] | |
| negative_articles = [] | |
| neutral_articles = [] | |
| for article in articles_data: | |
| summary = article["summary"].strip() or article["title"].split(" - ")[0].strip() | |
| source = article["title"].split(" - ")[-1].strip() if " - " in article["title"] else "" | |
| if source in summary: | |
| summary = summary.replace(source, "").strip() | |
| summary = f"{summary.rstrip(' -')} - {source}" | |
| sentiment = analyze_sentiment(summary) | |
| topics = extract_topics(summary) | |
| sentiments[sentiment] += 1 | |
| title = article["title"].split(" - ")[0].strip() | |
| if sentiment == "Positive": | |
| positive_articles.append(title) | |
| elif sentiment == "Negative": | |
| negative_articles.append(title) | |
| else: | |
| neutral_articles.append(title) | |
| articles.append({ | |
| "Title": article["title"], | |
| "Summary": summary, | |
| "Sentiment": sentiment, | |
| "Topics": topics, | |
| "Link": article["link"], | |
| "PubDate": article["pub_date"] | |
| }) | |
| detailed_comparisons = [f"- News {i + 1} {article['Sentiment'].lower()}ly discusses {', '.join(article['Topics'])}" | |
| for i, article in enumerate(articles)] | |
| dominant_sentiment = max(sentiments, key=sentiments.get) | |
| trends = f"{company} News Trends: {dominant_sentiment}" | |
| total_articles = sum(sentiments.values()) | |
| sentiment_count = f"{sentiments['Positive']} positive, {sentiments['Negative']} negative, {sentiments['Neutral']} neutral" | |
| intro_phrases = [ | |
| f"Spanning {total_articles} recent reports, the narrative surrounding {company} tilts {dominant_sentiment.lower()}, with {sentiment_count}.", | |
| f"Across {total_articles} articles in recent coverage, {company}’s story emerges as predominantly {dominant_sentiment.lower()}, reflecting {sentiment_count}.", | |
| f"Drawing from {total_articles} latest publications, {company}’s news landscape leans {dominant_sentiment.lower()}, underscored by {sentiment_count}." | |
| ] | |
| positive_phrases = [ | |
| f"With {len(positive_articles)} favorable accounts, {company} demonstrates notable progress, exemplified by '{random.choice(positive_articles) if positive_articles else 'no specific examples available'}'.", | |
| f"Boasting {len(positive_articles)} positive developments, {company} showcases strength, as evidenced in '{random.choice(positive_articles) if positive_articles else 'no notable instances'}'.", | |
| f"Highlighted by {len(positive_articles)} encouraging reports, {company} is forging ahead, with '{random.choice(positive_articles) if positive_articles else 'no standout reports'}' standing out." | |
| ] | |
| negative_phrases = [ | |
| f"However, {len(negative_articles)} troubling narratives raise concerns, including '{random.choice(negative_articles) if negative_articles else 'no specific concerns noted'}'.", | |
| f"Yet, {len(negative_articles)} adverse reports signal challenges, such as '{random.choice(negative_articles) if negative_articles else 'no highlighted issues'}'.", | |
| f"Nevertheless, {len(negative_articles)} concerning stories cast a shadow, notably '{random.choice(negative_articles) if negative_articles else 'no notable setbacks'}'." | |
| ] | |
| neutral_phrases = [ | |
| f"Additionally, {len(neutral_articles)} impartial updates provide context, such as '{random.choice(neutral_articles) if neutral_articles else 'no neutral updates available'}'.", | |
| f"Meanwhile, {len(neutral_articles)} balanced accounts offer insight, including '{random.choice(neutral_articles) if neutral_articles else 'no balanced reports'}'.", | |
| f"Furthermore, {len(neutral_articles)} objective pieces contribute details, like '{random.choice(neutral_articles) if neutral_articles else 'no objective details'}'." | |
| ] | |
| outlook_phrases_positive = [ | |
| f"In summary, {company} appears poised for a favorable trajectory.", | |
| f"All told, {company} stands on the cusp of a promising future.", | |
| f"Ultimately, {company} is positioned for an optimistic course ahead." | |
| ] | |
| outlook_phrases_negative = [ | |
| f"In conclusion, {company} confronts a challenging path forward.", | |
| f"Overall, {company} navigates a formidable road ahead.", | |
| f"To conclude, {company} faces a demanding horizon." | |
| ] | |
| outlook_phrases_mixed = [ | |
| f"In the final analysis, {company} balances opportunity and uncertainty.", | |
| f"On balance, {company} presents a complex outlook moving forward.", | |
| f"Ultimately, {company} reflects a blend of prospects and hurdles." | |
| ] | |
| final_text = random.choice(intro_phrases) + " " | |
| if positive_articles: | |
| final_text += random.choice(positive_phrases) + " " | |
| if negative_articles: | |
| final_text += random.choice(negative_phrases) + " " | |
| if neutral_articles: | |
| final_text += random.choice(neutral_phrases) + " " | |
| if sentiments["Positive"] > sentiments["Negative"]: | |
| final_text += random.choice(outlook_phrases_positive) | |
| elif sentiments["Negative"] > sentiments["Positive"]: | |
| final_text += random.choice(outlook_phrases_negative) | |
| else: | |
| final_text += random.choice(outlook_phrases_mixed) | |
| print(f"Generated dynamic final sentiment for {company}: {final_text}") | |
| return { | |
| "Company": company, | |
| "Articles": articles, | |
| "Comparative Sentiment Score": { | |
| "Sentiment Distribution": f"Positive: {sentiments['Positive']}, Negative: {sentiments['Negative']}, Neutral: {sentiments['Neutral']}", | |
| "Trends": trends, | |
| "Detailed Comparisons": "\n".join(detailed_comparisons) | |
| }, | |
| "Final Sentiment Analysis": final_text.strip(), | |
| "Audio": None | |
| } | |
| if __name__ == "__main__": | |
| import uvicorn | |
| uvicorn.run(app, host="0.0.0.0", port=8000) # Start the API server on port 8000 |