app.py

import gradio as gr
import requests
import pandas as pd
from PIL import Image
import numpy as np
import base64

API_URL = "https://api-inference.huggingface.co/models/AliGhiasvand86/gisha_digit_recognition"
headers = {"Authorization": "Bearer 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"}

def query(image_path):
    try:
        with open(image_path, "rb") as file:
            response = requests.post(API_URL, headers=headers, data=file.read())
        response.raise_for_status()  # Check for HTTP error
        data = response.json()
        print(data)  # Print the response data for debugging
        final_resp = []
        for i in data:
            resp = {}
            resp["Number predicted"] = i['label']
            resp["probability"] = i['score']

            final_resp.append(resp)
        print(final_resp)
        return final_resp
    except Exception as e:
        return {"Error": f"An error occurred: {e}"}



def save_array_as_image(array, image_path):
    # Convert the array to an image
    image = Image.fromarray(array)
    
    # Save the image to the specified path
    image.save(image_path)

def classify_digit(image):
    # Save the image as a .png file
    image_path = "sketchpad.png"
    save_array_as_image(image, image_path)
    
    result = query(image_path)
    return pd.DataFrame.from_records(result)

iface = gr.Interface(fn=classify_digit, inputs='sketchpad', outputs=gr.outputs.Dataframe(),
                   allow_flagging='never', description='Draw a Digit Below... (Draw in the centre for best results)',
                   layout="horizontal")
iface.launch()