Update app.py

app.py

@@ -41,11 +41,8 @@ class_names = ['BacterialBlights', 'Healthy', 'Mosaic', 'RedRot', 'Rust', 'Yello
 
 # Function to predict disease type from an image
 def predict_disease(image):
-    # Open the image file
-    img = Image.open(image)
-    
     # Apply transformations to the image
-    img_tensor = transform(img).unsqueeze(0)  # Add batch dimension
+    img_tensor = transform(image).unsqueeze(0)  # Add batch dimension
 
     # Make prediction
     with torch.no_grad():
@@ -57,21 +54,23 @@ def predict_disease(image):
     
     return predicted_label
 
-# Gradio interface
-inputs = gr.inputs.Image(type="pil")
-outputs = gr.outputs.Text()
+# Create Gradio interface
+inputs = gr.Image(type="pil")
+outputs = gr.Text()
 
-EXAMPLES = ["img1.png"]
+EXAMPLES = ["img1.png", "img2.png", "img3.png", "img4.png"]
 
 demo_app = gr.Interface(
-    fn=predict_disease, 
+    fn=predict_disease,
     inputs=inputs,
     outputs=outputs,
-    title="Sugarcane Disease Prediction with ViT",
+    title="Sugarcane Disease Detection",
     examples=EXAMPLES,
+    cache_example=True,
     live=True,
     theme="huggingface"
 )
 
 demo_app.launch(debug=True, enable_queue=True)
 
+