Update app.py

app.py

@@ -1,21 +1,46 @@
+import cv2
+import gradio as gr
 import tensorflow as tf
 import numpy as np
-from urllib.request import urlretrieve
-import gradio as gr
 
-urlretrieve("https://gr-models.s3-us-west-2.amazonaws.com/mnist-model.h5", "mnist-model.h5")
-model = tf.keras.models.load_model("mnist-model.h5")
-
-def recognize_digit(image):
-    image = image.reshape(1, -1)  # add a batch dimension
-    prediction = model.predict(image).tolist()[0]
-    return {str(i): prediction[i] for i in range(10)}
-
-gr.Interface(fn=recognize_digit, 
-             inputs="sketchpad", 
-             outputs=gr.outputs.Label(num_top_classes=3),
-             live=True,
-             css=".footer {display:none !important}",
-             # title="MNIST Sketchpad",
-             description="Draw a number 0 through 9 on the sketchpad, and see predictions in real time.",
-             thumbnail="https://raw.githubusercontent.com/gradio-app/real-time-mnist/master/thumbnail2.png").launch();
+# Model yükleniyor
+model = tf.keras.models.load_model("number_recognition_model_colab.keras")
+
+# Etiketler (0'dan 9'a kadar sayılar)
+labels = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"]
+
+# Tahmin fonksiyonu
+def predict(img):
+    try:
+        # Görüntüyü gri tonlamaya dönüştür
+        if img.ndim == 3 and img.shape[-1] == 3:
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        elif img.ndim == 2:
+            img = np.expand_dims(img, axis=-1)
+
+        # Görüntüyü yeniden boyutlandır ve normalize et
+        img = cv2.resize(img, (28, 28))
+        img = img.astype('float32') / 255.0
+        img = img.reshape(1, 28, 28, 1)
+
+        # Modelden tahmin al
+        preds = model.predict(img)[0]
+
+        # Tahmin sonuçlarını formatla
+        sorted_preds = sorted(zip(labels, preds), key=lambda x: x[1], reverse=True)[:3]
+        formatted_preds = "\n".join([f"{label}: {prob:.2f}" for label, prob in sorted_preds])
+
+        return formatted_preds
+    except Exception as e:
+        return f"Error: {e}"
+
+# Gradio arayüzü
+interface = gr.Interface(
+    fn=predict,
+    inputs="sketchpad",
+    outputs="textbox",
+    title="Sketch Recognition App",
+    description="Draw a number (0-9) and see the model's top predictions."
+)
+
+interface.launch(debug=True)