Update app.py

app.py

@@ -4,6 +4,7 @@ import tensorflow as tf  # version 2.13.0
 from keras.models import load_model
 import cv2
 import json
+import os
 
 def analyse(img, plant_type):
     # Load label_disease.json
@@ -66,6 +67,21 @@ def analyse(img, plant_type):
 
     return result
 
+def get_example_images():
+    examples = []
+    example_dir = "examples"
+    
+    # Load all images from examples directory
+    if os.path.exists(example_dir):
+        image_files = [f for f in os.listdir(example_dir) if f.endswith(('.jpg', ,'JPG','.jpeg', '.png'))]
+        for img_file in image_files:
+            # Extract plant type from filename (assumes filename format: planttype_condition.jpg)
+            plant_type = img_file.split('_')[0].capitalize()
+            img_path = os.path.join(example_dir, img_file)
+            examples.append([img_path, plant_type])
+    
+    return examples
+
 # Gradio interface
 demo = gr.Interface(
     fn=analyse,
@@ -74,7 +90,13 @@ demo = gr.Interface(
         gr.Radio(["Apple", "Blueberry", "Cherry", "Corn", "Grape", "Orange", "Peach",
                   "Pepper", "Potato", "Raspberry", "Soybean", "Squash", "Strawberry", "Tomato"])
     ],
-    outputs=gr.JSON()
+    outputs=gr.JSON(),
+    examples=get_example_images(),
+    title="Plant Disease Detection",
+    description="""Upload an image of a plant leaf or use one of the example images below. 
+                Select the type of plant and the model will analyze it for diseases."""
 )
 
-demo.launch(share=True, show_error=True)
+# Launch the interface
+if __name__ == "__main__":
+    demo.launch(share=True, show_error=True)