app.py

import gradio as gr
import torch
import pytesseract
from transformers import AutoTokenizer, AutoModel

# Set Tesseract executable path
pytesseract.pytesseract.tesseract_cmd = r'/opt/homebrew/bin/tesseract'

# Load the tokenizer and model
tokenizer_eng = AutoTokenizer.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True)
model_eng = AutoModel.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True).eval()

def perform_ocr(image, language):
    # Convert the Gradio image input to the format suitable for pytesseract
    img_cv = image  # Assuming image is already in the correct format

    if language == "English":
        # Perform OCR using the model for English
        res_eng = model_eng.chat(tokenizer_eng, img_cv, ocr_type='ocr')
        return res_eng  # Return results for English
    elif language == "Hindi":
        # Perform OCR using pytesseract for Hindi
        res_hin = pytesseract.image_to_string(img_cv, lang='hin', config='--psm 6')
        return res_hin  # Return results for Hindi
    else:
        return "Unsupported language selected."

def ocr_and_search(image, language):
    # Call the perform_ocr function
    extracted_text = perform_ocr(image, language)
    # You may also want to implement any searching functionality here
    # ...
    
    return extracted_text  # Return the OCR result for the selected language

# Create Gradio interface
iface = gr.Interface(
    fn=ocr_and_search,
    inputs=[
        gr.Image(type="numpy", label="Upload Image"),
        gr.Dropdown(choices=["English", "Hindi"], label="Select Language")
    ],
    outputs=gr.Textbox(label="Extracted Text"),
    title="OCR Application",
    description="Upload an image to extract text using OCR."
)

# Run the app
if __name__ == "__main__":
    iface.launch()