.

app.py

@@ -18,6 +18,9 @@ def load_breeds(file_path=class_file_path):
 
 labels = load_breeds()
 
+# Prepare disease list text
+disease_list_text = "**Supported Diseases:**\n\n" + "\n".join([label.replace('_', ' ').title() for label in labels])
+
 def process_image(image, img_size=224):
     img_array = tf.keras.preprocessing.image.img_to_array(image)
     img_array = tf.image.resize(img_array, [img_size, img_size]) / 255.0
@@ -26,7 +29,7 @@ def process_image(image, img_size=224):
 def predict_breed(image):
     try:
         if image is None:
-            return "❌ No image uploaded. Please upload a Maize or Tomato leaf image.", {}
+            return "❌ No image uploaded. Please upload a Maize or Tomato leaf image.", {}, disease_list_text
 
         img_array = process_image(image)
         img_array = tf.expand_dims(img_array, axis=0)
@@ -36,7 +39,7 @@ def predict_breed(image):
 
         top_pred_class = labels[top3_indices[0]]
         if "maize" not in top_pred_class.lower() and "tomato" not in top_pred_class.lower():
-            return "❌ This model only supports Maize and Tomato leaf images.", {}
+            return "❌ This model only supports Maize and Tomato leaf images.", {}, disease_list_text
 
         output_lines = ["🌿 **Top 3 Predictions:**"]
         confidence_scores = {}
@@ -47,16 +50,20 @@ def predict_breed(image):
             output_lines.append(f"{class_name}: {confidence:.2f}%")
             confidence_scores[class_name] = float(f"{confidence:.2f}")
 
-        return "\n".join(output_lines), confidence_scores
+        return "\n".join(output_lines), confidence_scores, disease_list_text
 
     except Exception as e:
         print("Prediction Error:", e)
-        return "❌ File not supported. Please upload a valid image file (JPEG/PNG).", {}
+        return "❌ File not supported. Please upload a valid image file (JPEG/PNG).", {}, disease_list_text
 
 interface = gr.Interface(
     fn=predict_breed,
     inputs=gr.Image(type="pil", label="Upload Maize or Tomato Leaf Image"),
-    outputs=[gr.Text(label="Prediction"), gr.Label(label="Confidence Scores")],
+    outputs=[
+        gr.Text(label="Prediction"),
+        gr.Label(label="Confidence Scores"),
+        gr.Markdown(label="Supported Diseases")
+    ],
     title="🌿 Hares: Maize & Tomato Disease Classifier",
     description="Upload an image of a maize or tomato leaf to identify possible diseases.",
 )