Update app.py

app.py

@@ -2,37 +2,75 @@ import cv2
 import gradio as gr
 import tensorflow as tf
 import numpy as np
+import PIL.Image
+
+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>"
+)
 
 # Model yükleniyor
 model = tf.keras.models.load_model("number_recognition_model_colab.keras")
 
+
+img_size = 28
 # 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 the input is a dictionary (Gradio sketchpad), extract the image
+        if isinstance(img, dict):
+            img = img.get('image', None)  # Get the image from the dictionary
+
+        if img is None:
+            raise ValueError("No image data found")
+
+        # 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)
 
-        # Görüntüyü yeniden boyutlandır ve normalize et
-        img = cv2.resize(img, (28, 28))
+        # 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, 28, 28, 1)
+        img = img.reshape(1, img_size, img_size, 1)
+
+        # Print the shape after resizing and normalizing
+        print(f"Processed image shape: {img.shape}")
 
-        # Modelden tahmin al
+        # Get the predictions from the model
         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])
+        # Print the predictions
+        print("Predictions:", preds)
 
-        return formatted_preds
+        # Return the predictions for each label
+        return {label: float(pred) for label, pred in zip(labels, preds)}
     except Exception as e:
-        return f"Error: {e}"
+        # Print the exception to the console
+        print(f"Error during prediction: {e}")
+        return {"Error": str(e)}
+
+# Set up the Gradio interface with the input as a sketchpad and output as labels
+label = gr.Label(num_top_classes=3)
 
 # Gradio arayüzü
 interface = gr.Interface(