Update app.py

app.py

@@ -8,7 +8,6 @@ from PIL import Image
 import requests
 from io import BytesIO
 from fastapi import FastAPI
-from gradio.routes import App
 
 # Define the number of classes
 num_classes = 2
@@ -18,11 +17,13 @@ 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")))
@@ -44,22 +45,16 @@ transform = transforms.Compose([
 # Function to predict from image content
 def predict_from_image(image):
     try:
-        # Log the image processing
         print(f"Processing image: {image}")
-        
-        # Ensure the image is a PIL Image
         if not isinstance(image, Image.Image):
             raise ValueError("Invalid image format received. Please provide a valid image.")
 
         # Apply transformations
         image_tensor = transform(image).unsqueeze(0)
-
-        # Predict
         with torch.no_grad():
             outputs = model(image_tensor)
             predicted_class = torch.argmax(outputs, dim=1).item()
 
-        # Interpret the result
         if predicted_class == 0:
             return {"result": "The photo is of fall army worm with problem ID 126."}
         elif predicted_class == 1:
@@ -74,11 +69,10 @@ def predict_from_image(image):
 # Function to predict from URL
 def predict_from_url(url):
     try:
-        # Fetch the image from the URL
+        print(f"Fetching image from URL: {url}")
         response = requests.get(url)
-        response.raise_for_status()  # Ensure the request was successful
+        response.raise_for_status()
         image = Image.open(BytesIO(response.content))
-        print(f"Fetched image from URL: {url}")
         return predict_from_image(image)
     except Exception as e:
         print(f"Error during URL processing: {e}")
@@ -87,21 +81,19 @@ def predict_from_url(url):
 # Main prediction function with caching
 def predict(image, url):
     try:
+        print("Starting prediction...")
         if image:
             result = predict_from_image(image)
         elif url:
             result = predict_from_url(url)
         else:
             result = {"error": "No input provided. Please upload an image or provide a URL."}
-        
-        # Generate and store the event ID
+
         event_id = id(result)  # Use Python's id() function to generate a unique identifier
         results_cache[event_id] = result
-        
-        # Log the result
+
         print(f"Event ID: {event_id}, Result: {result}")
         return {"event_id": event_id, "result": result}
-
     except Exception as e:
         print(f"Error in prediction function: {e}")
         return {"error": str(e)}
@@ -109,7 +101,7 @@ def predict(image, url):
 # Function to retrieve result by event_id
 def get_result(event_id):
     try:
-        # Convert event_id from string to int
+        print(f"Retrieving result for event ID: {event_id}")
         event_id = int(event_id)
         result = results_cache.get(event_id)
         if result: