Update app.py

app.py

@@ -3,32 +3,28 @@ import gradio as gr
 import tensorflow as tf
 import numpy as np
 
-# Başlık ve açıklama
-title = "Welcome on your first sketch recognition app!"
-description = (
-    "The robot was trained to classify numbers (from 0 to 9). "
-    "To test it, write your number in the space provided."
+title = "Welcome to your first sketch recognition app!"
+head = (
+    "<center>"
+    "The robot was trained to classify numbers (from 0 to 9). To test it, write your number in the space provided."
+    "</center>"
 )
+ref = "Find the whole code [here](https://github.com/ovh/ai-training-examples/tree/main/apps/gradio/sketch-recognition)."
 
-article = "Find the whole code [here](https://github.com/ovh/ai-training-examples/tree/main/apps/gradio/sketch-recognition)."
-
-# Model parametreleri
 img_size = 28
 labels = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"]
 
-# Model yükleme
+# Model yükleniyor
 model = tf.keras.models.load_model("number_recognition_model_colab.keras")
 
-# Tahmin fonksiyonu
 def predict(img):
     try:
-        # Görüntüyü işleme
+        # Girdi görselini NumPy array'e çevir
         if not isinstance(img, np.ndarray):
             img = np.array(img)
 
-        if img.ndim == 3 and img.shape[-1] == 3:
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-
+        # Görüntüyü gri tonlamaya çevir ve yeniden boyutlandır
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) if img.ndim == 3 else img
         img = cv2.resize(img, (img_size, img_size))
         img = img.astype('float32') / 255.0
         img = img.reshape(1, img_size, img_size, 1)
@@ -39,18 +35,16 @@ def predict(img):
     except Exception as e:
         return {"Error": str(e)}
 
-# Gradio arayüzü
+label = gr.Label(num_top_classes=3)
+
+# Yeni Gradio bileşenleriyle uyumlu hale getirildi
 interface = gr.Interface(
     fn=predict,
-    inputs=gr.inputs.Sketchpad(label="Draw a number"),  # Kullanıcının sayı çizebileceği alan
-    outputs=gr.outputs.Label(num_top_classes=3, label="Predicted Number"),  # Tahminlerin gösterileceği alan
+    inputs=gr.Sketchpad(label="Draw a number"),  # Sketchpad kullanımı
+    outputs=label,
     title=title,
-    description=description,
-    article=article,
-    allow_flagging="manual",  # Flagging'i etkinleştir
-    flagging_options=["Incorrect Prediction", "Other Issues"],  # Kullanıcıların seçebileceği flag nedenleri
-    flagging_dir="flagged_results"  # Flag sonuçlarını kaydedeceği klasör
+    description=head,
+    article=ref
 )
 
-# Uygulamayı başlat
-interface.launch()
+interface.launch(debug=True)