Update app.py

app.py

@@ -1,17 +1,65 @@
 import gradio as gr
+import torch
+from torch import nn
+from torchvision import models, transforms
+from huggingface_hub import hf_hub_download
+from PIL import Image
 
-def process_image(image):
-    # For now, this function will just return a placeholder response
-    return "Placeholder output for uploaded 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)
 
-# Define the Gradio interface
+# Download model from Hugging Face
+def download_model():
+    model_path = hf_hub_download(repo_id="jays009/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
+    return model
+
+# Download the model and load it
+model_path = download_model()  # Downloads the model from Hugging Face Hub
+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)
+])
+
+# 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
+    
+    with torch.no_grad():
+        outputs = model(image)  # Perform forward pass
+        predicted_class = torch.argmax(outputs, dim=1).item()  # Get the predicted class
+    
+    # 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:
+        return "The photo you've sent is of a healthy wheat image."
+    else:
+        return "Unexpected class prediction."
+
+# Create the Gradio interface
 iface = gr.Interface(
-    fn=process_image,  # Function to process the image
-    inputs=gr.Image(type="pil", label="Upload Image"),  # Accepts image input
-    outputs=gr.Textbox(label="Prediction"),  # Displays text output
-    title="Image Input Example",
-    description="Upload an image to get a prediction. Placeholder logic for now.",
+    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."
 )
 
-# Launch the interface
+# Launch the Gradio interface
 iface.launch()