Update app.py

app.py

@@ -1,26 +1,32 @@
-from flask import Flask, render_template, request, jsonify
+from flask import Flask, request, jsonify, render_template
 import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
 import base64
 from io import BytesIO
 from PIL import Image
-from tensorflow.keras.models import load_model
 
-# Load trained VGG16 model
-model = load_model("best_model.h5")
+app = Flask(__name__)
 
-# Class names from your training dataset
-class_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
+# Load your model
+model = load_model('./best_model.h5', compile=False)
 
-app = Flask(__name__)
+# Itsekiri digit labels
+itsekiri_labels = [
+    'Méene', 'Méji', 'Métà', 'Mérin', 'Márùn',
+    'Méfa', 'Méje', 'Méjọ', 'Méṣan', 'Méwà'
+]
 
-def preprocess_image(img_data):
-    """Decode base64, resize to (228, 228), rescale 0-1, add batch dim"""
-    img_data = img_data.split(',')[1]
-    img_bytes = base64.b64decode(img_data)
+IMG_SIZE = (228, 228)
+
+def preprocess_image(base64_str):
+    header, encoded = base64_str.split(',', 1)
+    img_bytes = base64.b64decode(encoded)
     img = Image.open(BytesIO(img_bytes)).convert('RGB')
-    img = img.resize((228, 228))
-    img_array = np.array(img, dtype=np.float32) / 255.0  # rescale
+    img = img.resize(IMG_SIZE)
+    img_array = image.img_to_array(img)
     img_array = np.expand_dims(img_array, axis=0)
+    img_array = img_array.astype('float32') / 255.0
     return img_array
 
 @app.route('/')
@@ -29,19 +35,17 @@ def index():
 
 @app.route('/predict', methods=['POST'])
 def predict():
-    data = request.get_json()
-    image_data = data['image']
-    img_array = preprocess_image(image_data)
-
-    predictions = model.predict(img_array)
-    pred_idx = int(np.argmax(predictions[0]))
-    pred_label = class_names[pred_idx]
-    confidence = float(predictions[0][pred_idx] * 100)
-
+    data = request.get_json(force=True)
+    img_data = data['image']
+    img_array = preprocess_image(img_data)
+    pred_probs = model.predict(img_array)
+    pred_index = np.argmax(pred_probs)
+    pred_label = itsekiri_labels[pred_index]
+    confidence = float(pred_probs[0][pred_index] * 100)
     return jsonify({
         'prediction': pred_label,
         'confidence': confidence
     })
 
 if __name__ == '__main__':
-    app.run(host="0.0.0.0", port=7860, debug=False)
+    app.run(host='0.0.0.0', port=7860)