Update app.py

app.py

@@ -1,28 +1,55 @@
 import tensorflow as tf
 import gradio as gr
 import numpy as np
+import cv2
 
-# Load the CNN model from the .h5 file
+# Load model
 model = tf.keras.models.load_model('TP_MNIST_CNN_model.h5')
 
-def predict_digit(image):
-    # Preprocess the input image
-    image = np.expand_dims(image, axis=0)  # Add batch dimension
-    image = image / 255.0  # Normalize pixel values
+def preprocess_and_predict(input_data):
+    if input_data is None:
+        return None
+        
+    # Gradio 4.x Sketchpad returns a dict usually: {'composite': array, 'layers': [...]}
+    # We take the composite image
+    if isinstance(input_data, dict):
+        image = input_data['composite']
+    else:
+        image = input_data
 
-    # Make predictions
-    predictions = model.predict(image)
+    # 1. Resize to MNIST standard (28x28)
+    # Interpolation AREA is better for shrinking images without losing thin lines
+    image = cv2.resize(image, (28, 28), interpolation=cv2.INTER_AREA)
 
-    # Get the predicted digit
-    predicted_digit = np.argmax(predictions)
+    # 2. Convert to Grayscale (if RGB/RGBA)
+    if len(image.shape) == 3:
+        image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
 
-    return predicted_digit
+    # 3. Normalize & Invert Logic
+    # MNIST is White digits on Black background.
+    # If user draws Black on White, we must invert.
+    # Check mean pixel intensity: if high (>127), background is likely white.
+    if np.mean(image) > 127:
+        image = 255 - image
+        
+    # 4. Normalize 0-1
+    image = image / 255.0
 
-# Define Gradio interface
-gr.Interface(
-    title="Reconnaissance d'écriture manuscrite des données MNIST by PSW",
-    fn=predict_digit, 
-    inputs=gr.Sketchpad(label="Desinner le chiffre ici",  height=350, width=800),
-    outputs="number",
-    live=True
-).launch(debug=True)
+    # 5. Reshape for Model (Batch, Height, Width, Channels)
+    image = image.reshape(1, 28, 28, 1)
+
+    # Prediction
+    predictions = model.predict(image, verbose=0)
+    return int(np.argmax(predictions))
+
+# Modern Gradio 4 Interface
+iface = gr.Interface(
+    fn=preprocess_and_predict,
+    inputs=gr.Sketchpad(label="Dessinez un chiffre", type="numpy"),
+    outputs="label",
+    title="MNIST Digit Recognition",
+    description="Reconnaissance de chiffres manuscrits via CNN. Dessinez au centre.",
+    allow_flagging="never"
+)
+
+iface.launch()