Update app.py

app.py

@@ -5,24 +5,17 @@ import requests
 import torch
 
 # Load model from Hugging Face model hub
-model_name = "your-username/your-model-name"  # Replace with your model's name on Hugging Face
+model_name = "best20.pt"  # Replace with your model's name on Hugging Face
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForImageClassification.from_pretrained(model_name)
 
 # Define function for image preprocessing and prediction
 def process_image(image):
-    # Load and preprocess image
-    image = Image.open(image)
-    inputs = tokenizer(image, return_tensors="pt", padding=True, truncation=True)
-    # Make prediction
-    outputs = model(**inputs)
-    predicted_class = torch.argmax(outputs.logits, dim=1)
-    return predicted_class.item()
+   img_4d=img.reshape(-1,256,256,3)
+  prediction=Model.predict(img_4d)[0]
+  return {class_names[i]: float(prediction[i]) for i in range(3)}
 
-# Create Gradio interface
-inputs = gr.inputs.Image()
-output = gr.outputs.Label(num_top_classes=1)
-interface = gr.Interface(fn=process_image, inputs=inputs, outputs=output, capture_session=True)
+image = gr.inputs.Image(shape=(256,256))
+label = gr.outputs.Label(num_top_classes=3)
 
-# Deploy the Gradio interface
-interface.launch(share=True)
+gr.Interface(fn=predict_image, inputs=image, outputs=label,interpretation='default').launch()