Update app.py

app.py

@@ -2,15 +2,18 @@ import gradio as gr
 import tensorflow as tf
 import numpy as np
 from PIL import Image
+import os
 
-# ==========================
-# LOAD MODEL
-# ==========================
-model = tf.keras.models.load_model("orange_disease_model.h5")
+# Fix compatibility
+os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0"
 
-# ==========================
-# CLASS NAMES (MODIFIER SELON TON MODELE)
-# ==========================
+# Load model
+model = tf.keras.models.load_model(
+    "orange_disease_model.h5",
+    compile=False
+)
+
+# Classes (MODIFIER)
 class_names = [
     "Healthy",
     "Black spot",
@@ -19,53 +22,30 @@ class_names = [
     "Melanose"
 ]
 
-# ==========================
-# IMAGE SIZE (MODIFIER SELON TON MODELE)
-# ==========================
-IMG_SIZE = 224
+IMG_SIZE = 256
+
 
-# ==========================
-# PREDICTION FUNCTION
-# ==========================
 def predict(image):
 
-    # Convert to RGB
     image = image.convert("RGB")
-
-    # Resize
     image = image.resize((IMG_SIZE, IMG_SIZE))
 
-    # Convert to array
-    img_array = np.array(image)
-
-    # Normalize
-    img_array = img_array / 255.0
-
-    # Expand dims
+    img_array = np.array(image) / 255.0
     img_array = np.expand_dims(img_array, axis=0)
 
-    # Predict
     prediction = model.predict(img_array)
 
-    confidence = float(np.max(prediction))
     class_index = int(np.argmax(prediction))
+    confidence = float(np.max(prediction))
 
-    result = class_names[class_index]
+    return f"Prediction: {class_names[class_index]} | Confidence: {confidence:.4f}"
 
-    return f"Prediction: {result} | Confidence: {confidence:.4f}"
 
-# ==========================
-# GRADIO INTERFACE
-# ==========================
-interface = gr.Interface(
+demo = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="pil"),
     outputs="text",
-    title="Orange Disease Detection",
-    description="Upload image of orange leaf to detect disease"
+    title="Orange Disease Detection API"
 )
 
-# ==========================
-# LAUNCH
-# ==========================
-interface.launch()
+demo.launch()