Update app.py

app.py

@@ -107,59 +107,99 @@ class ImageProcessor:
     """Handles image preprocessing for better OCR results"""
     
     @staticmethod
-    def preprocess_receipt_image(image_path):
-        """
-        Preprocess receipt image for optimal OCR
-        Returns: processed image path and preprocessing info
-        """
+    def process_receipt_image(image_file, phone):
+    """
+    Complete receipt processing pipeline that handles Gradio file objects
+    Returns: (success, status_message, extracted_data, image_preview)
+    """
+    try:
+        if not phone:
+            return False, "❌ Please sign in first", {}, None
+        
+        if not image_file:
+            return False, "❌ No image uploaded", {}, None
+
+        # Debug input type
+        print(f"\n📁 Input type: {type(image_file)}")
+        
+        # Handle different input types
+        if isinstance(image_file, str):
+            # Case 1: Direct file path (local testing)
+            image_path = image_file
+        elif hasattr(image_file, 'name'):
+            # Case 2: Gradio NamedString object (Hugging Face Spaces)
+            image_path = image_file.name
+        else:
+            return False, "❌ Unsupported file input type", {}, None
+
+        # Create receipts directory if needed
+        os.makedirs(RECEIPTS_DIR, exist_ok=True)
+        
+        # Generate unique filename
+        timestamp = int(time.time())
+        filename = f"receipt_{phone}_{timestamp}{os.path.splitext(image_path)[1]}"
+        save_path = os.path.join(RECEIPTS_DIR, filename)
+        
+        # Copy the uploaded file (works for both Gradio and direct paths)
+        with open(image_path, 'rb') as src, open(save_path, 'wb') as dst:
+            dst.write(src.read())
+        
+        print(f"📄 Saved receipt to: {save_path}")
+        
+        # Preprocess image
+        processed_path, preprocessing_info = ImageProcessor.preprocess_receipt_image(save_path)
+        print(f"🖼️ Preprocessing: {preprocessing_info}")
+        
+        # Extract text using OCR
+        raw_text, confidence, extracted_data = ocr_service.extract_text_from_receipt(processed_path)
+        print(f"🔍 OCR Confidence: {confidence:.1%}")
+        
+        # Auto-categorize
+        if extracted_data.get('merchant'):
+            suggested_category = db.auto_categorize_receipt(
+                phone, 
+                extracted_data['merchant'], 
+                extracted_data.get('total_amount', 0)
+            )
+            extracted_data['suggested_category'] = suggested_category
+            print(f"🏷️ Suggested category: {suggested_category}")
+        
+        # Prepare receipt data for database
+        receipt_data = {
+            'image_path': save_path,
+            'processed_image_path': processed_path,
+            'merchant': extracted_data.get('merchant', ''),
+            'amount': extracted_data.get('total_amount', 0.0),
+            'date': extracted_data.get('date', ''),
+            'category': extracted_data.get('suggested_category', 'Miscellaneous'),
+            'confidence': confidence,
+            'raw_text': raw_text,
+            'extracted_data': extracted_data,
+            'is_validated': False
+        }
+        
+        # Save to database
+        receipt_id = db.save_receipt(phone, receipt_data)
+        extracted_data['receipt_id'] = receipt_id
+        print(f"💾 Saved to DB with ID: {receipt_id}")
+        
+        # Create image preview
         try:
-            if not PIL_AVAILABLE:
-                return image_path, "No preprocessing - PIL not available"
-            
-            # Load image
-            image = Image.open(image_path)
-            
-            # Convert to RGB if needed
-            if image.mode != 'RGB':
-                image = image.convert('RGB')
-            
-            # Enhance contrast
-            enhancer = ImageEnhance.Contrast(image)
-            image = enhancer.enhance(1.5)
-            
-            # Enhance sharpness
-            enhancer = ImageEnhance.Sharpness(image)
-            image = enhancer.enhance(2.0)
-            
-            # Convert to grayscale
-            image = image.convert('L')
-            
-            # Apply Gaussian blur to reduce noise
-            image = image.filter(ImageFilter.GaussianBlur(radius=0.5))
-            
-            # Convert to numpy array for OpenCV processing
-            if 'cv2' in globals() and cv2 is not None:
-                img_array = np.array(image)
-                
-                # Apply threshold to get binary image
-                _, binary = cv2.threshold(img_array, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
-                
-                # Morphological operations to clean up the image
-                kernel = np.ones((1,1), np.uint8)
-                binary = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel)
-                
-                # Convert back to PIL Image
-                image = Image.fromarray(binary)
-            
-            # Save processed image
-            processed_path = image_path.replace('.', '_processed.')
-            image.save(processed_path)
-            
-            return processed_path, "Enhanced contrast, sharpness, applied thresholding"
-            
+            image_preview = Image.open(save_path)
+            image_preview.thumbnail((400, 600))  # Resize for display
         except Exception as e:
-            print(f"Image preprocessing error: {e}")
-            return image_path, f"Preprocessing failed: {str(e)}"
+            print(f"⚠️ Preview generation failed: {e}")
+            image_preview = None
+        
+        status_msg = f"✅ Receipt processed! Confidence: {confidence:.1%}"
+        if confidence < 0.7:
+            status_msg += " ⚠️ Low confidence - please verify"
+        
+        return True, status_msg, extracted_data, image_preview
+        
+    except Exception as e:
+        print(f"❌ Processing error: {traceback.format_exc()}")
+        return False, f"❌ Processing failed: {str(e)}", {}, None
     
     @staticmethod
     def extract_text_regions(image_path):