import numpy as np
import json
import gradio as gr
import easyocr

reader = easyocr.Reader(['en'])

# ocr = PaddleOCR(use_angle_cls=True, lang='en', use_pdserving=False, cls_batch_num=8, det_batch_num=8, rec_batch_num=8)

# ocr = PaddleOCR(use_angle_cls=True, lang='en')

# def index(url):
#     response = requests.get(url)
#     img = Image.open(BytesIO(response.content))
#     resize_factor = 1
#     new_size = tuple(int(dim * resize_factor) for dim in img.size)
#     img = img.resize(new_size, Image.Resampling.LANCZOS)

#     img_array = np.array(img.convert('RGB'))

#     result = ocr.ocr(img_array)

#     boxes = [line[0] for line in result]
#     txts = [line[1][0] for line in result]
#     scores = [line[1][1] for line in result]

#     print(boxes)
#     print(txts)

#     output_dict = {"texts": txts, "boxes": boxes, "scores": scores}
#     output_json = json.dumps(output_dict)  # Convert to JSON string

#     return output_json


def index(image_url):
    result = reader.readtext(image_url)

    texts = []
    probs = []

    for (bbox, text, prob) in result:
        # print(f'Text: {text}, Probability: {prob}')
        texts.append(text)
        probs.append(prob)

    output_dict = {"texts": texts, "boxes": probs}
    output_json = json.dumps(output_dict) 
    
    return output_json

inputs_image_url = [
    gr.Textbox(type="text", label="Image URL"),
]

outputs_result_json = [
    gr.Textbox(type="text", label="Result JSON"),
]

interface_image_url = gr.Interface(
    fn=index,
    inputs=inputs_image_url,
    outputs=outputs_result_json,
    title="Text Extraction",
    cache_examples=False,
).queue().launch()