Update app.py

app.py

@@ -5,45 +5,40 @@ from torchvision import models, transforms
 from huggingface_hub import hf_hub_download
 from PIL import Image
 
-# Define the number of classes
-num_classes = 2  # Update with the actual number of classes in your dataset (e.g., 2 for healthy and anomalous)
+num_classes = 2  
 
-# Download model from Hugging Face
 def download_model():
-    model_path = hf_hub_download(repo_id="jays009/jays009/Restnet50", filename="pytorch_model.bin")
+    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):
-    model = models.resnet50(pretrained=False)  # Set pretrained=False because you're loading custom weights
-    model.fc = nn.Linear(model.fc.in_features, num_classes)  # Adjust for the number of classes in your dataset
-    model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))  # Load model on CPU for compatibility
-    model.eval()  # Set to evaluation mode
+    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")))  
+    model.eval() 
     return model
 
-# Download the model and load it
-model_path = download_model()  # Downloads the model from Hugging Face Hub
+model_path = download_model()  
 model = load_model(model_path)
 
-# Define the transformation for the input image
+
 transform = transforms.Compose([
     transforms.Resize(256),  # Resize the image to 256x256
     transforms.CenterCrop(224),  # Crop the image to 224x224
     transforms.ToTensor(),  # Convert the image to a Tensor
-    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),  # Normalize the image (ImageNet mean and std)
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),  
 ])
 
-# Define the prediction function
 def predict(image):
-    # Apply the necessary transformations to the image
-    image = transform(image).unsqueeze(0)  # Add batch dimension
-    image = image.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))  # Move to GPU if available
+    
+    image = transform(image).unsqueeze(0)  
+    image = image.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))  
     
     with torch.no_grad():
-        outputs = model(image)  # Perform forward pass
-        predicted_class = torch.argmax(outputs, dim=1).item()  # Get the predicted class
+        outputs = model(image)  
+        predicted_class = torch.argmax(outputs, dim=1).item()  
+
     
-    # Create a response based on the predicted class
     if predicted_class == 0:
         return "The photo you've sent is of fall army worm with problem ID 126."
     elif predicted_class == 1:
@@ -51,15 +46,13 @@ def predict(image):
     else:
         return "Unexpected class prediction."
 
-# Create the Gradio interface
 iface = gr.Interface(
-    fn=predict,  # Function for prediction
-    inputs=gr.Image(type="pil"),  # Image input
-    outputs=gr.Textbox(),  # Output: Predicted class
-    live=True,  # Updates as the user uploads an image
-    title="Wheat Anomaly Detection",
-    description="Upload an image of wheat to detect anomalies like disease or pest infestation."
+    fn=predict,  
+    inputs=gr.Image(type="pil"),  
+    outputs=gr.Textbox(),  
+    live=True,  
+    title="Maize Anomaly Detection",
+    description="Upload an image of maize to detect anomalies like disease or pest infestation."
 )
 
-# Launch the Gradio interface
 iface.launch()