Update app.py

app.py

@@ -3,48 +3,30 @@ import gradio as gr
 import tensorflow as tf
 import numpy as np
 
-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)."
 
-img_size = 28
-labels = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"]
 
 # Model yükleniyor
 model = tf.keras.models.load_model("number_recognition_model_colab.keras")
 
 def predict(img):
-    try:
-        # Girdi görselini NumPy array'e çevir
-        if not isinstance(img, np.ndarray):
-            img = np.array(img)
-
-        # 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)
-
-        preds = model.predict(img)[0]
-
-        return {label: float(pred) for label, pred in zip(labels, preds)}
-    except Exception as e:
-        return {"Error": str(e)}
-
-label = gr.Label(num_top_classes=3)
-
-# Yeni Gradio bileşenleriyle uyumlu hale getirildi
-interface = gr.Interface(
-    fn=predict,
-    inputs=gr.Sketchpad(label="Draw a number"),  # Sketchpad kullanımı
-    outputs=label,
-    title=title,
-    description=head,
-    article=ref
-)
-
-interface.launch(debug=True)
+    # Preprocess the input image
+    img = img.reshape(1, 28, 28) / 255.0
+
+    # Make the prediction
+    prediction = model.predict(img)
+    predicted_digit = np.argmax(prediction[0])
+
+    return predicted_digit
+
+# Create the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("Welcome on your first sketch recognition app!")
+    
+    with gr.Row():
+        sketchpad = gr.Sketchpad(shape=(28, 28))
+        output = gr.Text()
+    
+    btn = gr.Button("Submit")
+    btn.click(predict, inputs=sketchpad, outputs=output)
+
+demo.launch()