app.py

import gradio as gr
# from transformers import pipeline
from PIL import Image
import pytesseract
import cv2
import os

# pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")

# def predict(input_img):
#     predictions = pipeline(input_img)
#     return input_img, {p["label"]: p["score"] for p in predictions} 

def recognize(input_img):
    text = pytesseract.image_to_string(Image.open("./data/" + filename))
    return input_img, text

gradio_app = gr.Interface(
    recognize,
    inputs=gr.inputs.Textbox(lines=2, placeholder="Name Here..."),
    inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],

    inputs=[gr.Image(label="Upload an Image", type="pil")],
    outputs=[gr.Textbox(label="Text in the Image")],
    
    title="Extrate Text From Image",
)

if __name__ == "__main__":
    gradio_app.launch()