Update app.py

app.py

@@ -1,9 +1,20 @@
+app_content = """
 import gradio as gr
 import tensorflow as tf
 import numpy as np
+import os
 
-# 1. Load the model and class names
-model = tf.keras.models.load_model('plantvillage_efficientnet_b0.keras')
+# 1. Load the model with error handling
+try:
+    model_path = 'plantvillage_efficientnet_b0.keras'
+    if not os.path.exists(model_path):
+        raise FileNotFoundError(f"Model file {model_path} not found in Space!")
+    
+    model = tf.keras.models.load_model(model_path, compile=False)
+    print("Model loaded successfully!")
+except Exception as e:
+    print(f"CRITICAL ERROR LOADING MODEL: {e}")
+    model = None
 
 classes = [
     'Pepper__bell___Bacterial_spot', 'Pepper__bell___healthy', 'Potato___Early_blight',
@@ -15,6 +26,9 @@ classes = [
 ]
 
 def predict(image):
+    if model is None:
+        return "Error: Model failed to load. Check Space logs."
+    
     img = tf.cast(image, tf.float32)
     img = tf.image.resize(img, (224, 224))
     img = tf.keras.applications.efficientnet.preprocess_input(img)
@@ -23,14 +37,19 @@ def predict(image):
     preds = model.predict(img)[0]
     return {classes[i]: float(preds[i]) for i in range(len(classes))}
 
-# 2. Build Interface
 demo = gr.Interface(
     fn=predict,
     inputs=gr.Image(),
     outputs=gr.Label(num_top_classes=3),
     title="Plant Disease Detector",
-    description="Identify plant leaf diseases using EfficientNetB0."
+    description="Identify plant leaf diseases."
 )
 
 if __name__ == '__main__':
-    demo.launch()
+    demo.launch()
+"""
+
+with open('app.py', 'w') as f:
+    f.write(app_content.strip())
+
+print('app.py has been updated with extra error checking. Download and upload it to HF!')