Update app.py

app.py

@@ -3,18 +3,11 @@ import tensorflow as tf
 from tensorflow.keras.applications.efficientnet import preprocess_input
 from tensorflow.keras.preprocessing import image
 import numpy as np
-import zipfile
-import os
-
-# 🔓 Automatically unzip example images if needed
-if not os.path.exists("examples") and os.path.exists("examples.zip"):
-    with zipfile.ZipFile("examples.zip", 'r') as zip_ref:
-        zip_ref.extractall("examples")
 
 # 🧠 Load model
 model = tf.keras.models.load_model("efficientnet_final_model.keras")
 
-# 🍃 Disease class labels
+# 🍃 Class names
 CLASS_NAMES = [
     "Pomegranate__diseased", "mango_Sooty Mould", "mango_Powdery Mildew", 
     "mango_Healthy", "mango_Gall Midge", "mango_Die Back", 
@@ -37,29 +30,20 @@ def predict_disease(img):
     label = CLASS_NAMES[top_idx]
     return f"{label} ({confidence:.2f}%)"
 
-# 🎛️ Gradio Interface
+# 🎛️ Gradio interface
 interface = gr.Interface(
     fn=predict_disease,
     inputs=gr.Image(type="pil"),
     outputs="text",
     title="🌿 Fruit Leaf Disease Classifier",
-    description="Upload an image of a fruit or leaf to detect its disease type.",
-    allow_flagging="never"
-)
-
-# 🖼️ Show image examples using Gradio v4+ Examples class
-interface.examples = gr.Examples(
+    description="Upload a fruit or leaf image to identify the disease.",
     examples=[
-        ["examples/Phytopthora (97).jpg"],
-        ["examples/Red Rust(60).jpg"],
-        ["examples/20211231_162315 (Custom).jpg"],
-        ["examples/0021_0060.jpg"]
+        ["Phytopthora.jpg"],
+        ["RedRust.jpg"]
     ],
-    inputs=interface.input_components,
-    cache_examples=False
+    cache_examples=False,
+    allow_flagging="never"
 )
 
-# 🚀 Launch the app
 if __name__ == "__main__":
     interface.launch()
-