Update app.py

app.py

@@ -1,4 +1,5 @@
 ### 1. Imports and class names setup ### 
+### 1. Imports and class names setup ###
 import gradio as gr
 import os
 import torch
@@ -12,89 +13,82 @@ class_names = ['bacterial', 'blast', 'brownspot', 'tungro']
 
 ### 2. Model and transforms preparation ###
 
-# Create EffNetB2 model
 mobilenet, manual_transforms = create_mobilenet_model(
-    num_classes=4, # len(class_names) would also work
+    num_classes=4
 )
 
-# Load saved weights
 mobilenet.load_state_dict(
     torch.load(
         f="mobilenet_5_epochs.pth",
-        map_location=torch.device("cpu"),  # load to CPU
+        map_location=torch.device("cpu"),
     )
 )
 
 ### 3. Predict function ###
-
-# Create predict function
 def predict(img) -> Tuple[Dict, float]:
-    """Transforms and performs a prediction on img and returns prediction and time taken.
-    """
-    # Start the timer
     start_time = timer()
     
-    # Transform the target image and add a batch dimension
     img = manual_transforms(img).unsqueeze(0)
     
-    # Put model into evaluation mode and turn on inference mode
     mobilenet.eval()
     with torch.inference_mode():
-        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
         pred_probs = torch.softmax(mobilenet(img), dim=1)
     
-    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
     pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
     
-    # Calculate the prediction time
     pred_time = round(timer() - start_time, 5)
     
-    # Return the prediction dictionary and prediction time 
     return pred_labels_and_probs, pred_time
 
 ### 4. Gradio app ###
-# Gradio interface
 
-def app():
-    with gr.Blocks():
-        with gr.Row():
-            with gr.Column():
-                image = gr.Image(type="pil", label="Image")
-                infer = gr.Button(value="Submit")
+# Create a Blocks app (only one!)
+with gr.Blocks() as gradio_app:
     
-            with gr.Column():
-                output_image = [gr.Label(num_top_classes=4, label="Predictions"), # what are the outputs?
-                              gr.Number(label="Prediction time (s)")],   # our fn has two outputs, therefore we have two outputs
-            with gr.Column():
-                example_list = [["examples/" + example] for example in os.listdir("examples")]
-                examples = gr.Examples(examples=example_list, 
-                                       inputs=gr.Image(type='pil') )
-        infer.click(
-            fn=predict,
-            inputs=image,
-            outputs=output_image,
-  
-        )
-#[gr.Textbox(label="greeting", lines=1)], gr.Image(image_mode="RGB")
-gradio_app = gr.Blocks()
-
-with gradio_app:
     gr.HTML(
         """
-    <h1 style='text-align: center'>
-    SIAMESE: Real-Time End-to-End Face Security System
-    </h1>
-    """)
+        <h1 style='text-align: center'>
+        Rice Disease Detection - MobileNet Model
+        </h1>
+        """
+    )
+    
     gr.HTML(
         """
         <h3 style='text-align: center'>
         Follow me for more!
-        <a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a>  | <a href='https://www.huggingface.co/kadirnar/' target='_blank'>HuggingFace</a>
+        <a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | 
+        <a href='https://github.com/kadirnar' target='_blank'>Github</a> | 
+        <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a>  | 
+        <a href='https://www.huggingface.co/kadirnar/' target='_blank'>HuggingFace</a>
         </h3>
-        """)
+        """
+    )
+    
     with gr.Row():
         with gr.Column():
-            app()
+            image = gr.Image(type="pil", label="Upload Image")
+            infer = gr.Button(value="Predict")
+            
+            # Examples linked to the input component 'image'
+            example_list = [["examples/" + example] for example in os.listdir("examples")]
+            gr.Examples(
+                examples=example_list,
+                inputs=[image]  # Pass the actual input component
+            )
+
+        with gr.Column():
+            label = gr.Label(num_top_classes=4, label="Predictions")
+            pred_time = gr.Number(label="Prediction Time (s)")
+        
+        infer.click(
+            fn=predict,
+            inputs=[image],
+            outputs=[label, pred_time]
+        )
+
+# Launch the app
+gradio_app.launch()
 # gradio_app.launch(debug=True, share=True)
 # # Create title, description and article strings
 # title = "RICE DISEASES CLASSIFICATION"