Update app.py

app.py

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+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
+import os
+import logging
+import requests
+from io import BytesIO
+
+# Setup logging
+logging.basicConfig(level=logging.INFO)
+
+# Define the number of classes
+num_classes = 3
+
+# Download model from Hugging Face
+def download_model():
+    model_path = hf_hub_download(repo_id="jays009/Resnet3", filename="model.pth")
+    return model_path
+
+# Load the model from Hugging Face
+def load_model(model_path):
+    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()
+    logging.info("Model loaded successfully. Ready for inference.")
+    return model
+
+# Download the model and load it
+model_path = download_model()
+model = load_model(model_path)
+
+# Define the transformation for the input image
+transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+])
+
+# Prediction function for an uploaded image
+
+def predict_from_image_url(image_url):
+    try:
+        # Download the image from the provided URL
+        response = requests.get(image_url)
+        response.raise_for_status()
+        image = Image.open(BytesIO(response.content))
+
+        # Apply transformations
+        image_tensor = transform(image).unsqueeze(0)
+
+        # Perform prediction
+        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:
+            return {"result": "The photo shows symptoms of Phosphorus Deficiency with Problem ID 142."}
+        elif predicted_class == 2:
+            return {"result": "The photo shows symptoms of Bacterial Leaf Blight with Problem ID 203."}
+        else:
+            return {"error": "Unexpected class prediction."}
+
+    except Exception as e:
+        return {"error": str(e)}
+
+
+demo = gr.Interface(
+    fn=predict_from_image_url,
+    inputs="text",
+    outputs="json",
+    title="Maize Disease Classification",
+    description="Enter a URL to an image for classification (Fall Army Worm, Phosphorus Deficiency, or Bacterial Leaf Blight).",
+)
+
+if __name__ == "__main__":
+    demo.launch()