Update app.py

app.py

@@ -3,9 +3,18 @@ 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
 
+# 🔓 Unzip examples.zip if not already extracted
+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 trained model
 model = tf.keras.models.load_model("efficientnet_final_model.keras")
 
+# 🍃 Class names
 CLASS_NAMES = [
     "Pomegranate__diseased", "mango_Sooty Mould", "mango_Powdery Mildew", 
     "mango_Healthy", "mango_Gall Midge", "mango_Die Back", 
@@ -16,6 +25,7 @@ CLASS_NAMES = [
     "lime_Scab", "lime_Anthracnose", "lime_Sooty Mould"
 ]
 
+# 🔍 Prediction function
 def predict_disease(img):
     img = img.resize((160, 160))
     img_array = image.img_to_array(img)
@@ -29,12 +39,13 @@ def predict_disease(img):
 
     return f"{label} ({confidence:.2f}%)"
 
+# 🎛️ 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/leaf and the model will classify the disease type.",
+    title="🌿 Fruit Leaf Disease Classifier",
+    description="Upload a fruit or leaf image to predict its disease type.",
     examples=[
         ["examples/Phytopthora.jpg"],
         ["examples/RedRust.jpg"],