Update app.py

app.py

@@ -17,13 +17,11 @@ results_cache = {}
 
 # Download model from Hugging Face
 def download_model():
-    print("Downloading model...")
     model_path = hf_hub_download(repo_id="jays009/Restnet50", filename="pytorch_model.bin")
     return model_path
 
 # Load the model from Hugging Face
 def load_model(model_path):
-    print("Loading model...")
     model = models.resnet50(pretrained=False)
     model.fc = nn.Linear(model.fc.in_features, num_classes)
     model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
@@ -78,24 +76,40 @@ def predict_from_url(url):
         print(f"Error during URL processing: {e}")
         return {"error": f"Failed to process the URL: {str(e)}"}
 
-# Main prediction function with caching
-def predict(image, url):
+# Main prediction function without event ID for direct uploads
+def predict_direct_upload(image):
     try:
-        print("Starting prediction...")
         if image:
             result = predict_from_image(image)
-        elif url:
-            result = predict_from_url(url)
+            return result
         else:
-            result = {"error": "No input provided. Please upload an image or provide a URL."}
-
-        event_id = id(result)  # Use Python's id() function to generate a unique identifier
-        results_cache[event_id] = result
+            return {"error": "No image provided. Please upload an image."}
+    except Exception as e:
+        print(f"Error in direct upload prediction function: {e}")
+        return {"error": str(e)}
 
-        print(f"Event ID: {event_id}, Result: {result}")
-        return {"event_id": event_id, "result": result}
+# Main prediction function with caching for paths via Postman
+def predict_with_event_id(data):
+    try:
+        path = data[0].get('path', None)
+        if path:
+            if path.startswith("http://") or path.startswith("https://"):
+                result = predict_from_url(path)
+            elif os.path.isfile(path):
+                image = Image.open(path)
+                result = predict_from_image(image)
+            else:
+                result = {"error": "Invalid path format. Please provide a valid URL or local file path."}
+            
+            event_id = id(result)
+            results_cache[event_id] = result
+            
+            print(f"Event ID: {event_id}, Result: {result}")
+            return {"event_id": event_id, "result": result}
+        else:
+            return {"error": "No path provided. Please provide a valid path."}
     except Exception as e:
-        print(f"Error in prediction function: {e}")
+        print(f"Error in prediction function with event ID: {e}")
         return {"error": str(e)}
 
 # Function to retrieve result by event_id
@@ -130,8 +144,8 @@ with iface:
     submit_button = gr.Button("Submit")
     
     submit_button.click(
-        fn=predict,
-        inputs=[image_input, url_input],
+        fn=predict_direct_upload,
+        inputs=[image_input],
         outputs=output
     )