File size: 1,176 Bytes
eb034fe
 
 
 
5667d33
eb034fe
5667d33
 
eb034fe
 
20006a6
eb034fe
5667d33
eb034fe
5667d33
eb034fe
 
5667d33
 
 
eb034fe
5667d33
eb034fe
 
5667d33
 
 
 
 
 
eb034fe
5667d33
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
import gradio as gr
from PIL import Image
import random

# Exemple de base de données de mots
WORDS_DATABASE = {
    "cat": {"fr": "chat", "audio": None},
    "house": {"fr": "maison", "audio": None},
}

# Simule un modèle de reconnaissance d'image
def recognize_object(image):
    return random.choice(list(WORDS_DATABASE.keys()))

# Fonction principale
def handle_drawing(image):
    recognized_object = recognize_object(image)
    french_translation = WORDS_DATABASE.get(recognized_object, {}).get("fr", "Inconnu")
    response = f"Objet reconnu : {recognized_object.capitalize()} (Français : {french_translation})"
    return response, None

# Interface utilisateur
def create_interface():
    with gr.Blocks() as demo:
        gr.Markdown("# Apprends l'Anglais en Dessinant !")
        canvas = gr.Sketchpad(label="Dessine ici !", shape=(256, 256))
        response_text = gr.Textbox(label="Résultat", interactive=False)
        submit_button = gr.Button("Reconnaître")
        submit_button.click(handle_drawing, inputs=[canvas], outputs=[response_text, None])
    return demo

# Lancement
if __name__ == "__main__":
    demo = create_interface()
    demo.launch()