app.py

# from flask import Flask, request, jsonify
# from handler import EndpointHandler

# app = Flask(__name__)
# handler = EndpointHandler()

# @app.route('/process_image', methods=['POST'])
# def process_image():
#     data = request.json
#     if data is None:
#         return jsonify({'error': 'No JSON data received'}), 400
    
#     try:
#         result_image = handler(data)
#     except Exception as e:
#         return jsonify({'error': str(e)}), 500
    
#     # Convert PIL image to base64 string
#     buffered = BytesIO()
#     result_image.save(buffered, format="JPEG")
#     result_image_str = base64.b64encode(buffered.getvalue()).decode()

#     return jsonify({'result_image': result_image_str})

# if __name__ == '__main__':
#     app.run(debug=False,port=8001)

import easyocr as ocr  #OCR
import streamlit as st  #Web App
from PIL import Image #Image Processing
import numpy as np #Image Processing 

#title
st.title("Easy OCR - Extract Text from Images")

#subtitle
st.markdown("## Optical Character Recognition - Using `easyocr`, `streamlit` -  hosted on 🤗 Spaces")

st.markdown("Link to the app - [image-to-text-app on 🤗 Spaces](https://huggingface.co/spaces/Amrrs/image-to-text-app)")

#image uploader
image = st.file_uploader(label = "Upload your image here",type=['png','jpg','jpeg'])


@st.cache
def load_model(): 
    reader = ocr.Reader(['en'],model_storage_directory='.')
    return reader 

reader = load_model() #load model

if image is not None:

    input_image = Image.open(image) #read image
    st.image(input_image) #display image

    with st.spinner("🤖 AI is at Work! "):
        

        result = reader.readtext(np.array(input_image))

        result_text = [] #empty list for results


        for text in result:
            result_text.append(text[1])

        st.write(result_text)
    #st.success("Here you go!")
    st.balloons()
else:
    st.write("Upload an Image")

st.caption("Made with ❤️ by @1littlecoder. Credits to 🤗 Spaces for Hosting this ")