restarting

app.py

@@ -1,4 +1,56 @@
-from ui import app  # import the Blocks object from ui.py
+import gradio as gr
+import numpy as np
+from model import BaselineCNN
+from data_pipeline import val_test_transform, IMAGE_SIZE
+import torch 
+
+from datasets import load_dataset
+dataset = load_dataset("DScomp380/plant_village", split="train")
+CLASS_NAMES = dataset.features["label"].names
+
+
+#load the model
+CLASSES = 39
+model = BaselineCNN(num_classes=CLASSES)
+model.load_state_dict(torch.load("best_model.pth", map_location=torch.device('cpu')))
+model.eval()
+
+def predict(input_image):
+    #apply the transform
+    image_tensor = val_test_transform(input_image)
+
+    #add batch dimension
+    image_tensor = image_tensor.unsqueeze(0)
+
+    #run inference 
+    with torch.no_grad():
+        output = model(image_tensor)
+    
+    #get probabilitiees
+    probabilities = torch.nn.functional.softmax(output,dim=1)[0]
+
+    #create the output dictionary
+    result = {CLASS_NAMES[i]: probabilities[i].item() for i in range(len(probabilities))}
+    return result
+
+
+with gr.Blocks(title="Plant Disease Classifier") as app:
+    gr.Markdown("# Plant Disease Classification")
+    gr.Markdown("Upload an image of a plant leaf to classify its disease.")
+
+    with gr.Row():
+        image_input = gr.Image(type="pil", label="Upload Leaf Image")
+        label_output = gr.Label(label="Predicted Disease")
+    
+    gr.Examples(
+        examples =[], inputs=image_input)
+    
+    submit_btn = gr.Button("Submit")
+    submit_btn.click(fn=predict, inputs=image_input, outputs=label_output)
+
+    #fn=predict, 
+    # inputs=gr.Image(type="pil"),
+    # outputs=gr.Label(num_top_classes=3))
 
 if __name__ == "__main__":
-    app.launch(ssr_mode=False, debug=False)  # disable SSR to avoid hot-reload 
+    app.launch()