import cv2
import mediapipe as mp
from mediapipe.tasks import python
from mediapipe.tasks.python import vision
import gradio as gr
import os

def recognize_gesture(image):
    # Load the gesture recognition model
    model_path = os.path.abspath("arabic_signlanguage_characters_model.task")
    recognizer = vision.GestureRecognizer.create_from_model_path(model_path)

    # Convert image to MediaPipe format
    if isinstance(image, gr.Image):  # Check if image is from Gradio
        image = image.to_ndarray(format="bgr")  # Convert to a NumPy array in BGR format

    # Convert image to MediaPipe format
    image = mp.Image(image_format=mp.ImageFormat.SRGB, data=image)

    # Perform gesture recognition
    recognition_result = recognizer.recognize(image)

    # Extract the top gesture
    top_gesture = recognition_result.gestures[0][0]

    # Return the gesture label and score
    return f"Gesture recognized: {top_gesture.category_name} ({top_gesture.score:.2f})"

iface = gr.Interface(
    fn=recognize_gesture,
    inputs=["image"],  # Input type: image
    outputs="text",  # Output type: text
    title="Arabic Sign Language Character Recognition",
    description="Upload an image to recognize the gesture",
)

iface.launch(share=True)  # Launch the interface in a web browser