Update tabs/single_prediction.py

tabs/single_prediction.py

@@ -1,77 +1,72 @@
-import gradio as gr
-from utils.predictions import predict_single_image, get_disease_info
-from utils.chart_vis import create_prediction_plot
-
-# this was written with the help of AI
-def create_single_prediction_tab(model, class_names, disease_db, device):
-    """Create the single image prediction tab"""
-    
-    def predict_with_visualization(image, show_top_n):
-        """Prediction function with all outputs"""
-        if image is None:
-            return None, "Please upload an image", None
-        
-        # Make prediction
-        top_preds = predict_single_image(image, model, class_names, device, show_top_n)
-        
-        # Create visualization
-        plot = create_prediction_plot(top_preds)
-        
-        # Get disease info
-        top_disease = top_preds[0][0]
-        confidence = top_preds[0][1]
-        
-        info_text = f"## Top Prediction: {top_disease}\n"
-        info_text += f"**Confidence:** {confidence:.2%}\n\n"
-        info_text += f"{get_disease_info(top_disease, disease_db)}\n\n"
-        
-        if confidence < 0.5:
-            info_text += "**Note:** Low confidence. Consider expert verification."
-        
-        # Results dictionary
-        results_dict = {label: round(float(prob), 4) for label, prob in top_preds}
-        
-        return plot, info_text, results_dict
-    
-    examples = [
-        ["ui_text/examples/Apple___Apple_scab.jpg"],
-        ["ui_text/examples/Tomato___healthy.jpg"],
-        ["ui_text/examples/Tomato___Bacterial_spot.jpg"],
-        ["ui_text/examples/Tomato___Tomato_Yellow_Leaf_Curl_Virus.jpg"],
-        ["ui_text/examples/Tomato___Septoria_leaf_spot.jpg"],
-        ["ui_text/examples/Soybean___healthy.jpg"]
-    ]
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            input_image = gr.Image(type="pil", label="Upload Plant Leaf Image")
-            top_n_slider = gr.Slider(
-                minimum=3, 
-                maximum=15, 
-                value=10, 
-                step=1, 
-                label="Number of top predictions to show"
-            )
-            predict_btn = gr.Button("Analyze Disease", variant="primary")
-            
-            gr.Markdown("### Example Images")
-            gr.Examples(
-                examples=examples,
-                inputs=input_image,
-                label="Click an example to try it out",
-                cache_examples=False
-            )
-        
-        with gr.Column(scale=1):
-            output_plot = gr.Plot(label="Prediction Confidence Chart")
-            output_info = gr.Markdown(label="Disease Information")
-            output_label = gr.Label(label="Detailed Predictions", num_top_classes=10)
-    
-    # Connect button
-    predict_btn.click(
-        fn=predict_with_visualization,
-        inputs=[input_image, top_n_slider],
-        outputs=[output_plot, output_info, output_label]
-    )
-    
+import gradio as gr
+from utils.predictions import predict_single_image, get_disease_info
+from utils.chart_vis import create_prediction_plot
+
+def create_single_prediction_tab(model, class_names, disease_db, device):
+    """Create the single image prediction tab"""
+    
+    def predict_with_visualization(image, show_top_n):
+        """Prediction function with all outputs"""
+        if image is None:
+            return None, "Please upload an image", None
+        
+        top_preds = predict_single_image(image, model, class_names, device, show_top_n)
+        
+        plot = create_prediction_plot(top_preds)
+        
+        top_disease = top_preds[0][0]
+        confidence = top_preds[0][1]
+        
+        info_text = f"## Top Prediction: {top_disease}\n"
+        info_text += f"**Confidence:** {confidence:.2%}\n\n"
+        info_text += f"{get_disease_info(top_disease, disease_db)}\n\n"
+        
+        if confidence < 0.5:
+            info_text += "**Note:** Low confidence. Consider expert verification."
+        
+        # Results dictionary
+        results_dict = {label: round(float(prob), 4) for label, prob in top_preds}
+        
+        return plot, info_text, results_dict
+    
+    examples = [
+        ["ui_text/examples/Apple___Apple_scab.jpg"],
+        ["ui_text/examples/Tomato___healthy.jpg"],
+        ["ui_text/examples/Tomato___Bacterial_spot.jpg"],
+        ["ui_text/examples/Tomato___Tomato_Yellow_Leaf_Curl_Virus.jpg"],
+        ["ui_text/examples/Tomato___Septoria_leaf_spot.jpg"],
+        ["ui_text/examples/Soybean___healthy.jpg"]
+    ]
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            input_image = gr.Image(type="pil", label="Upload Plant Leaf Image")
+            top_n_slider = gr.Slider(
+                minimum=3, 
+                maximum=15, 
+                value=10, 
+                step=1, 
+                label="Number of top predictions to show"
+            )
+            predict_btn = gr.Button("Analyze Disease", variant="primary")
+            
+            gr.Markdown("### Example Images")
+            gr.Examples(
+                examples=examples,
+                inputs=input_image,
+                label="Click an example to try it out",
+                cache_examples=False  # Disable caching to avoid duplicates
+            )
+        
+        with gr.Column(scale=1):
+            output_plot = gr.Plot(label="Prediction Confidence Chart")
+            output_info = gr.Markdown()
+            output_label = gr.Label(label="Detailed Predictions", num_top_classes=10)
+    
+    predict_btn.click(
+        fn=predict_with_visualization,
+        inputs=[input_image, top_n_slider],
+        outputs=[output_plot, output_info, output_label]
+    )
+    
     return input_image, top_n_slider, predict_btn