Update app.py

app.py

@@ -1,63 +1,56 @@
-import cv2
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-
-title = "Welcome on 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 = tf.keras.models.load_model("number_recognition_model_colab.keras")
-
-def predict(img):
-    try:
-        # Convert the input image to a NumPy array if needed
-        if not isinstance(img, np.ndarray):
-            img = np.array(img)
-
-        # Print shape and type of the input image
-        print(f"Initial image type: {type(img)}, shape: {img.shape}")
-
-        # Ensure the image is in grayscale and has a single channel
-        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)
-
-        # Print the shape of the grayscale image
-        print(f"Grayscale image shape: {img.shape}")
-
-        # Resize the image
-        img = cv2.resize(img, (img_size, img_size))
-
-        # Normalize the image
-        img = img.astype('float32') / 255.0
-        img = img.reshape(1, img_size, img_size, 1)
-
-        # Print the shape after resizing and normalizing
-        print(f"Processed image shape: {img.shape}")
-
-        preds = model.predict(img)[0]
-
-        # Print the predictions
-        print("Predictions:", preds)
-
-        return {label: float(pred) for label, pred in zip(labels, preds)}
-    except Exception as e:
-        # Print the exception to the console
-        print(f"Error during prediction: {e}")
-        return {"Error": str(e)}
-
-label = gr.Label(num_top_classes=3)
-
-interface = gr.Interface(fn=predict, inputs="sketchpad", outputs=label, title=title, description=head, article=ref)
-interface.launch(debug=True)
+import cv2
+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."
+)
+
+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 = tf.keras.models.load_model("number_recognition_model_colab.keras")
+
+# Tahmin fonksiyonu
+def predict(img):
+    try:
+        # Görüntüyü işleme
+        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)
+
+        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)}
+
+# Gradio arayüzü
+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
+    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
+)
+
+# Uygulamayı başlat
+interface.launch()