Create app.py

app.py

@@ -0,0 +1,28 @@
+import gradio as gr
+import cv2  # OpenCV library for image processing
+import tensorflow as tf
+from tensorflow.keras.applications.resnet50 import preprocess_input
+from tensorflow.keras.preprocessing import image
+import numpy as np
+ 
+model = tf.keras.models.load_model('cnn_resnet50_model.h5')
+ 
+def classify_image(img):
+    try:
+        # Resize the input image to match the expected input shape
+        img = cv2.resize(img, (128, 128))
+        # Convert the resized image to the appropriate format
+        img = image.img_to_array(img)
+        img = np.expand_dims(img, axis=0)
+        img = preprocess_input(img)
+        # Perform model inference
+        prediction = model.predict(img)
+        return {'class': np.argmax(prediction), 'confidence': np.max(prediction)}
+    except Exception as e:
+        print(f"Error: {e}")
+        return {'error': str(e)} 
+
+# Définir l'interface
+iface = gr.Interface(fn=classify_image, inputs="image", outputs="json").launch(share="True")
+
+