app.py

import cv2
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)