Update app.py

app.py

@@ -37,52 +37,21 @@ def predict(image):
     # Get the label name
     predicted_label = id2label[str(predicted_class_idx)]
     
-    # Return the predicted label and confidence score as a formatted string
-    return f"<h1 style='font-size: 2em; color: red;'>{predicted_label}<br>Confidence: {confidence_score:.2f}</h1>"
+    # Return the predicted label and confidence score
+    return f"{predicted_label}: {confidence_score:.2f}"
+
+    
+# Create the Gradio interface
 
 with gr.Blocks() as demo:
     with gr.Row():
         img1 = gr.Image(value="maladie_du_dragon_jaune.jpg", elem_id="img1")
         img2 = gr.Image(value="mineuse_des_agrumes.jpg", elem_id="img2")
-    
+
     with gr.Row():
-        input_img = gr.Image(type="pil", label="Upload an image of a citrus leaf to classify its disease.")
-        output = gr.Textbox(label="Disease Classification")
-    
+        input_img = gr.Image(type="pil", label="Upload an image of a citrus leaf to classify its disease. The model is trained on the following diseases: Aleurocanthus spiniferus, Chancre citrique, Cochenille blanche, Dépérissement des agrumes, Feuille saine, Jaunissement des feuilles, Maladie de l'oïdium, Maladie du dragon jaune, Mineuse des agrumes, Trou de balle.")
+        output = gr.Textbox(label="Prediction")
     btn = gr.Button("Classify")
-    
     btn.click(fn=predict, inputs=input_img, outputs=output)
 
-    css = """
-    <style>
-        #img1, #img2 {
-            width: 200px;
-            height: 200px;
-            border: 1px solid black;
-            border-radius: 10px;
-            cursor: pointer;
-        }
-        #img1:hover, #img2:hover {
-            border-color: blue;
-        }
-    </style>
-    """
-    js = """
-    <script>
-        document.getElementById("img1").addEventListener("dragstart", function(event) {
-            event.dataTransfer.setData("text", event.target.src);
-        });
-        document.getElementById("img2").addEventListener("dragstart", function(event) {
-            event.dataTransfer.setData("text", event.target.src);
-        });
-        document.getElementById("input_img").addEventListener("drop", function(event) {
-            event.preventDefault();
-            var data = event.dataTransfer.getData("text");
-            document.getElementById("input_img").src = data;
-        });
-    </script>
-    """
-    demo.html(css)
-    demo.html(js)
-
 demo.launch(share=True)