Delete working_ocr.py

working_ocr.py

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-# -*- coding: utf-8 -*-
-"""Working OCR.ipynb
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1uAUzcENIQdIKo5G6ZIBdDcaeL0c_5Qkz
-"""
-
-#!pip install git+https://github.com/huggingface/transformers
-
-#from google.colab import drive
-#drive.mount('/content/drive')
-
-!pip install --upgrade transformers
-
-!pip install -q torch flash-attn qwen-vl-utils spaces gradio tiktoken verovio
-
-import gradio as gr
-import spaces
-import json
-from transformers import AutoModel, AutoTokenizer, Qwen2VLForConditionalGeneration, AutoProcessor
-import torch
-from PIL import Image
-import numpy as np
-import os
-import base64
-import io
-import uuid
-import tempfile
-import time
-import shutil
-from pathlib import Path
-import tiktoken
-import verovio
-model_name = "ucaslcl/GOT-OCR2_0"
-tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
-model = AutoModel.from_pretrained(model_name, trust_remote_code=True, low_cpu_mem_usage=True, device_map='cuda', use_safetensors=True).eval().cuda()
-
-UPLOAD_FOLDER = "./uploads"
-RESULTS_FOLDER = "./results"
-
-for folder in [UPLOAD_FOLDER, RESULTS_FOLDER]:
-    if not os.path.exists(folder):
-        os.makedirs(folder)
-
-def image_to_base64(image):
-    buffered = io.BytesIO()
-    image.save(buffered, format="PNG")
-    return base64.b64encode(buffered.getvalue()).decode()
-
-q_model_name = "Qwen/Qwen2-VL-2B-Instruct"
-q_model = Qwen2VLForConditionalGeneration.from_pretrained(q_model_name, torch_dtype="auto").cuda().eval()
-q_processor = AutoProcessor.from_pretrained(q_model_name, trust_remote_code=True)
-
-def get_qwen_op(image_file, model, processor):
-    try:
-        image = Image.open(image_file).convert('RGB')
-        conversation = [
-            {
-                "role":"user",
-                "content":[
-                    {
-                        "type":"image",
-                    },
-                    {
-                        "type":"text",
-                        "text":"You are an accurate OCR engine. From the given image, extract the Hindi and other text."
-                    }
-                ]
-            }
-        ]
-        text_prompt = q_processor.apply_chat_template(conversation, add_generation_prompt=True)
-        inputs = q_processor(text=[text_prompt], images=[image], padding=True, return_tensors="pt").to("cuda")
-        inputs = {k: v.to(torch.float32) if torch.is_floating_point(v) else v for k, v in inputs.items()}
-
-        generation_config = {
-            "max_new_tokens": 1089,
-            "do_sample": False,
-            "top_k": 20,
-            "top_p": 0.90,
-            "temperature": 0.4,
-            "pad_token_id": q_processor.tokenizer.pad_token_id,
-            "eos_token_id": q_processor.tokenizer.eos_token_id,
-        }
-
-        output_ids = q_model.generate(**inputs, **generation_config)
-        if 'input_ids' in inputs:
-                generated_ids = output_ids[:, inputs['input_ids'].shape[1]:]
-        else:
-            generated_ids = output_ids
-
-        output_text = q_processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
-
-        return output_text[:] if output_text else "No text extracted from the image."
-
-    except Exception as e:
-        return f"An error occurred: {str(e)}"
-
-@spaces.GPU
-def run_GOT(image, got_mode, fine_grained_mode="", ocr_color="", ocr_box=""):
-    unique_id = str(uuid.uuid4())
-    image_path = os.path.join(UPLOAD_FOLDER, f"{unique_id}.png")
-    result_path = os.path.join(RESULTS_FOLDER, f"{unique_id}.html")
-
-    shutil.copy(image, image_path)
-
-    try:
-        if got_mode == "plain texts OCR":
-            res = model.chat(tokenizer, image_path, ocr_type='ocr')
-            return res, None
-        elif got_mode == "format texts OCR":
-            res = model.chat(tokenizer, image_path, ocr_type='format', render=True, save_render_file=result_path)
-        elif got_mode == "plain multi-crop OCR":
-            res = model.chat_crop(tokenizer, image_path, ocr_type='ocr')
-            return res, None
-        elif got_mode == "format multi-crop OCR":
-            res = model.chat_crop(tokenizer, image_path, ocr_type='format', render=True, save_render_file=result_path)
-        elif got_mode == "plain fine-grained OCR":
-            res = model.chat(tokenizer, image_path, ocr_type='ocr', ocr_box=ocr_box, ocr_color=ocr_color)
-            return res, None
-        elif got_mode == "format fine-grained OCR":
-            res = model.chat(tokenizer, image_path, ocr_type='format', ocr_box=ocr_box, ocr_color=ocr_color, render=True, save_render_file=result_path)
-        elif got_mode == "English + Hindi(Qwen2-VL)":
-            res = get_qwen_op(image_path, q_model, q_processor)
-            return res, None
-        # res_markdown = f"$$ {res} $$"
-        res_markdown = res
-
-        if "format" in got_mode and os.path.exists(result_path):
-            with open(result_path, 'r') as f:
-                html_content = f.read()
-            encoded_html = base64.b64encode(html_content.encode('utf-8')).decode('utf-8')
-            iframe_src = f"data:text/html;base64,{encoded_html}"
-            iframe = f'<iframe src="{iframe_src}" width="100%" height="600px"></iframe>'
-            download_link = f'<a href="data:text/html;base64,{encoded_html}" download="result_{unique_id}.html">Download Full Result</a>'
-            return res_markdown, f"{download_link}<br>{iframe}"
-        else:
-            return res_markdown, None
-    except Exception as e:
-        return f"Error: {str(e)}", None
-    finally:
-        if os.path.exists(image_path):
-            os.remove(image_path)
-
-def task_update(task):
-    if "fine-grained" in task:
-        return [
-            gr.update(visible=True),
-            gr.update(visible=False),
-            gr.update(visible=False),
-        ]
-    else:
-        return [
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-        ]
-
-def fine_grained_update(task):
-    if task == "box":
-        return [
-            gr.update(visible=False, value = ""),
-            gr.update(visible=True),
-        ]
-    elif task == 'color':
-        return [
-            gr.update(visible=True),
-            gr.update(visible=False, value = ""),
-        ]
-
-def search_in_text(text, keywords):
-    """Searches for keywords within the text and highlights matches."""
-
-    if not keywords:
-        return text
-
-    highlighted_text = text
-    for keyword in keywords.split():
-        highlighted_text = highlighted_text.replace(keyword, f"<mark>{keyword}</mark>")
-
-    return highlighted_text
-
-def cleanup_old_files():
-    current_time = time.time()
-    for folder in [UPLOAD_FOLDER, RESULTS_FOLDER]:
-        for file_path in Path(folder).glob('*'):
-            if current_time - file_path.stat().st_mtime > 3600:  # 1 hour
-                file_path.unlink()
-
-title_html = """ OCR Multilingual(GOT OCR 2.O) """
-
-with gr.Blocks() as demo:
-    gr.HTML(title_html)
-    gr.Markdown("""
-    by Souvik Biswas
-
-    ### Guidelines
-    Upload your image below and select your preferred mode. Note that more characters may increase wait times.
-    - **Plain Texts OCR & Format Texts OCR:** Use these modes for basic image-level OCR.
-    - **Plain Multi-Crop OCR & Format Multi-Crop OCR:** Ideal for images with complex content, offering higher-quality results.
-    - **Plain Fine-Grained OCR & Format Fine-Grained OCR:** These modes allow you to specify fine-grained regions on the image for more flexible OCR. Regions can be defined by coordinates or colors (red, blue, green, black or white).
-
-    """)
-
-    with gr.Row():
-        with gr.Column():
-            image_input = gr.Image(type="filepath", label="upload your image")
-            task_dropdown = gr.Dropdown(
-                choices=[
-                    "plain texts OCR",
-                    "format texts OCR",
-                    "plain multi-crop OCR",
-                    "format multi-crop OCR",
-                    "plain fine-grained OCR",
-                    "format fine-grained OCR",
-                    "English + Hindi(Qwen2-VL)"
-                ],
-                label="Choose one mode of GOT",
-                value="plain texts OCR"
-            )
-            fine_grained_dropdown = gr.Dropdown(
-                choices=["box", "color"],
-                label="fine-grained type",
-                visible=False
-            )
-            color_dropdown = gr.Dropdown(
-                choices=["red", "green", "blue", "black", "white"],
-                label="color list",
-                visible=False
-            )
-            box_input = gr.Textbox(
-                label="input box: [x1,y1,x2,y2]",
-                placeholder="e.g., [0,0,100,100]",
-                visible=False
-            )
-            submit_button = gr.Button("Submit")
-
-        with gr.Column():
-            ocr_result = gr.Textbox(label="GOT output")
-                # Create the Gradio interface
-            iface = gr.Interface(
-                fn=search_in_text,
-                inputs=[
-                    ocr_result,
-                    gr.Textbox(label="Keywords",
-                        placeholder="search keyword e.g., The",
-                        visible=True)],
-                outputs=gr.HTML(label="Search Results"),
-                allow_flagging="never"
-            )
-        with gr.Column():
-          if ocr_result.value:
-            with open("ocr_result.json", "w") as json_file:
-              json.dump({"text": ocr_result.value}, json_file) # Access the value of the Textbox using .value
-
-    with gr.Column():
-        gr.Markdown("**If you choose the mode with format, the mathpix result will be automatically rendered as follows:**")
-        html_result = gr.HTML(label="rendered html", show_label=True)
-
-    task_dropdown.change(
-        task_update,
-        inputs=[task_dropdown],
-        outputs=[fine_grained_dropdown, color_dropdown, box_input]
-    )
-    fine_grained_dropdown.change(
-        fine_grained_update,
-        inputs=[fine_grained_dropdown],
-        outputs=[color_dropdown, box_input]
-    )
-
-    submit_button.click(
-        run_GOT,
-        inputs=[image_input, task_dropdown, fine_grained_dropdown, color_dropdown, box_input],
-        outputs=[ocr_result, html_result]
-    )
-
-if __name__ == "__main__":
-    cleanup_old_files()
-    demo.launch()
-
-#!gradio deploy