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api.py

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+from fastapi import FastAPI, HTTPException
+from utils import generate_report
+from fastapi.middleware.cors import CORSMiddleware
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+@app.get("/report")
+async def get_report(company: str):
+    report, audio_file = generate_report(company)
+    if "error" in report:
+        raise HTTPException(status_code=404, detail=report["error"])
+    return {"report": report, "audio": audio_file}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="127.0.0.1", port=8000)

utils.py

@@ -0,0 +1,139 @@
+import os
+import requests
+from bs4 import BeautifulSoup
+from transformers import pipeline
+from gtts import gTTS
+from dotenv import load_dotenv
+import torch
+from collections import defaultdict
+import spacy
+
+load_dotenv()
+nlp = spacy.load("en_core_web_sm")  # For topic extraction
+
+# Initialize environment variables
+NEWS_API_KEY = os.environ.get('NEWS_API_KEY')
+
+def fetch_news(company):
+    """Fetch news articles using NewsAPI."""
+    url = f"https://newsapi.org/v2/everything?q={company}&apiKey={NEWS_API_KEY}"
+    response = requests.get(url)
+    if response.status_code != 200:
+        return []
+    articles = response.json().get('articles', [])
+    return articles
+
+def scrape_article(url):
+    """Scrape article title and content using BeautifulSoup."""
+    try:
+        response = requests.get(url, timeout=10)
+        soup = BeautifulSoup(response.content, 'html.parser')
+        title = soup.title.text.strip() if soup.title else "No Title"
+        paragraphs = soup.find_all('p')
+        content = ' '.join([p.text.strip() for p in paragraphs if p.text.strip()])
+        return title, content
+    except Exception as e:
+        print(f"Error scraping {url}: {e}")
+        return None, None
+
+# Load models
+sentiment_analyzer = pipeline("sentiment-analysis", 
+                            model="distilbert-base-uncased-finetuned-sst-2-english", 
+                            framework="pt")
+
+summarizer = pipeline("summarization", 
+                    model="facebook/bart-large-cnn",
+                    framework="pt")
+
+def analyze_sentiment(text):
+    """Analyze sentiment of the text."""
+    truncated_text = text[:512]
+    result = sentiment_analyzer(truncated_text)[0]
+    return result['label']
+
+def generate_report(company):
+    """Process company name to generate report with 10 unique articles."""
+    articles = fetch_news(company)
+    if not articles:
+        return {"error": "No articles found"}, None
+
+    report = {
+        "Company": company,
+        "Articles": [],
+        "Comparative Analysis": {
+            "Sentiment Distribution": {"Positive": 0, "Negative": 0, "Neutral": 0},
+            "Coverage Differences": [],
+            "Topic Overlap": {}
+        }
+    }
+    
+    unique_articles = []
+    seen_urls = set()
+    all_topics = []
+    
+    # Collect up to 10 unique articles
+    for article in articles:
+        url = article.get('url')
+        if url and url not in seen_urls:
+            seen_urls.add(url)
+            unique_articles.append(article)
+        if len(unique_articles) >= 10:
+            break
+
+    # Process articles
+    for article in unique_articles:
+        url = article.get('url')
+        title, content = scrape_article(url)
+        if not content:
+            continue
+
+        # Generate summary
+        try:
+            summary = summarizer(content, max_length=130, min_length=30)[0]['summary_text']
+        except:
+            summary = content[:100] + '...' if len(content) > 100 else content
+
+        # Analyze sentiment
+        sentiment = analyze_sentiment(content)
+        sentiment_key = "Positive" if sentiment == "POSITIVE" else "Negative" if sentiment == "NEGATIVE" else "Neutral"
+        report["Comparative Analysis"]["Sentiment Distribution"][sentiment_key] += 1
+
+        # Extract topics with spaCy
+        doc = nlp(content)
+        topics = [ent.text for ent in doc.ents if ent.label_ in ("ORG", "PRODUCT", "LAW")][:3]
+        all_topics.extend(topics)
+
+        report["Articles"].append({
+            "Title": title,
+            "Summary": summary,
+            "Sentiment": sentiment_key,
+            "Topics": topics
+        })
+
+    # Comparative Analysis
+    topic_counts = defaultdict(int)
+    for topic in all_topics:
+        topic_counts[topic] += 1
+
+    common_topics = [topic for topic, count in topic_counts.items() if count > 1]
+    unique_topics = list(set(all_topics))
+    
+    # Add coverage differences
+    if len(report["Articles"]) >= 2:
+        report["Comparative Analysis"]["Coverage Differences"].append({
+            "Comparison": f"{report['Articles'][0]['Title']} vs {report['Articles'][1]['Title']}",
+            "Impact": "Different aspects of the company covered"
+        })
+
+    report["Comparative Analysis"]["Topic Overlap"] = {
+        "Common Topics": common_topics,
+        "Unique Topics": unique_topics
+    }
+
+    # Generate Hindi TTS with gTTS
+    tts_text = f"{company} के लिए समाचार सारांश। सकारात्मक लेख: {report['Comparative Analysis']['Sentiment Distribution']['Positive']}, नकारात्मक: {report['Comparative Analysis']['Sentiment Distribution']['Negative']}, तटस्थ: {report['Comparative Analysis']['Sentiment Distribution']['Neutral']}."
+    tts = gTTS(tts_text, lang='hi')
+    tts_file = "summary_hi.mp3"
+    tts.save(tts_file)
+
+    return report, tts_file