/home/vscode/rickB/app.py

app.py

@@ -0,0 +1,262 @@
+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()