Update app.py

app.py

@@ -7,7 +7,7 @@ import pytesseract
 
 # Load YOLO model from HuggingFace
 repo_config = dict(
-    repo_id="arnabdhar/YOLOv8-nano-aadhar-card",
+    repo_id="arnabdhar/YOLOv8-nano-aadhar-card",  # Ensure the repo is accessible
     filename="model.pt",
     local_dir="./models"
 )
@@ -17,24 +17,28 @@ model = YOLO(hf_hub_download(**repo_config))
 id2label = model.names
 
 def predict(image):
-    image_np = np.array(image)
-    detections = model.predict(image_np)[0]
-    
-    results = []
-    for box, confidence, class_id in zip(detections.xyxy, detections.confidence, detections.class_id):
-        x1, y1, x2, y2 = map(int, box)
-        label = id2label[class_id]
-        
-        # Crop the detected region
-        cropped_region = image_np[y1:y2, x1:x2]
+    try:
+        image_np = np.array(image)
+        detections = model.predict(image_np)[0]
         
-        # Perform OCR on the cropped region
-        ocr_text = pytesseract.image_to_string(cropped_region, config='--psm 6')
+        results = []
+        for box, confidence, class_id in zip(detections.xyxy, detections.confidence, detections.class_id):
+            x1, y1, x2, y2 = map(int, box)
+            label = id2label[class_id]
+            
+            # Crop the detected region
+            cropped_region = image_np[y1:y2, x1:x2]
+            
+            # Perform OCR on the cropped region
+            ocr_text = pytesseract.image_to_string(cropped_region, config='--psm 6')
+            
+            results.append(f"Detected {label}: {ocr_text.strip()} with confidence {confidence:.2f}")
         
-        results.append(f"Detected {label}: {ocr_text.strip()} with confidence {confidence:.2f}")
+        return "\n".join(results)  # Return as a single string for easier display in Gradio
     
-    return results
+    except Exception as e:
+        return f"Error: {str(e)}"  # Basic error handling
 
 # Create Gradio interface
 iface = gr.Interface(fn=predict, inputs=gr.Image(type="pil"), outputs="text")
-iface.launch()
+iface.launch()