Create app.py

app.py

@@ -0,0 +1,141 @@
+"""
+Hugging Face Space Application
+Performs summarization (BART) and sentiment analysis on news articles
+"""
+
+from fastapi import FastAPI, HTTPException, Header
+from pydantic import BaseModel
+from transformers import pipeline
+import os
+from typing import Optional
+
+# Initialize FastAPI app
+app = FastAPI(title="News Analysis API")
+
+# Load models at startup (cached for performance)
+print("Loading models...")
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+print("Models loaded successfully!")
+
+# Request/Response models
+class ArticleRequest(BaseModel):
+    text: str
+    max_summary_length: Optional[int] = 150
+    min_summary_length: Optional[int] = 50
+
+class ArticleResponse(BaseModel):
+    summary: str
+    sentiment: str
+    sentiment_score: float
+    original_length: int
+    summary_length: int
+
+# Simple API key authentication (set in HF Space secrets)
+EXPECTED_API_KEY = os.environ.get("API_KEY", "your-secret-key-here")
+
+def verify_api_key(authorization: str = Header(None)):
+    """Verify the API key from Authorization header"""
+    if not authorization:
+        raise HTTPException(status_code=401, detail="Missing Authorization header")
+    
+    # Expected format: "Bearer <token>"
+    try:
+        scheme, token = authorization.split()
+        if scheme.lower() != "bearer" or token != EXPECTED_API_KEY:
+            raise HTTPException(status_code=401, detail="Invalid API key")
+    except ValueError:
+        raise HTTPException(status_code=401, detail="Invalid Authorization header format")
+    
+    return token
+
+@app.get("/")
+def read_root():
+    """Health check endpoint"""
+    return {
+        "status": "healthy",
+        "service": "News Analysis API",
+        "models": {
+            "summarization": "facebook/bart-large-cnn",
+            "sentiment": "distilbert-base-uncased-finetuned-sst-2-english"
+        }
+    }
+
+@app.post("/analyze", response_model=ArticleResponse)
+def analyze_article(
+    article: ArticleRequest,
+    authorization: str = Header(None)
+):
+    """
+    Analyze a news article: summarize and determine sentiment
+    
+    Args:
+        article: ArticleRequest with text to analyze
+        authorization: Bearer token for authentication
+    
+    Returns:
+        ArticleResponse with summary and sentiment
+    """
+    # Verify API key
+    verify_api_key(authorization)
+    
+    try:
+        # Validate input
+        if not article.text or len(article.text.strip()) < 50:
+            raise HTTPException(
+                status_code=400,
+                detail="Article text must be at least 50 characters"
+            )
+        
+        text = article.text.strip()
+        original_length = len(text)
+        
+        # STEP 1: Summarization using BART
+        # Truncate if text is too long (BART has max input length)
+        max_input_length = 1024
+        truncated_text = text[:max_input_length] if len(text) > max_input_length else text
+        
+        summary_result = summarizer(
+            truncated_text,
+            max_length=article.max_summary_length,
+            min_length=article.min_summary_length,
+            do_sample=False
+        )
+        
+        summary = summary_result[0]['summary_text']
+        
+        # STEP 2: Sentiment Analysis
+        # Use the summary for sentiment (more focused analysis)
+        sentiment_result = sentiment_analyzer(summary[:512])  # Sentiment model max length
+        
+        sentiment_label = sentiment_result[0]['label']  # POSITIVE or NEGATIVE
+        sentiment_score = sentiment_result[0]['score']
+        
+        # Return structured response
+        return ArticleResponse(
+            summary=summary,
+            sentiment=sentiment_label,
+            sentiment_score=round(sentiment_score, 4),
+            original_length=original_length,
+            summary_length=len(summary)
+        )
+        
+    except Exception as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Error processing article: {str(e)}"
+        )
+
+@app.get("/health")
+def health_check():
+    """Detailed health check"""
+    return {
+        "status": "healthy",
+        "models_loaded": True,
+        "summarizer": "ready",
+        "sentiment_analyzer": "ready"
+    }
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)