app.py

import cv2
import gradio as gr
import tensorflow as tf
import numpy as np

# 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)