app.py

import gradio as gr
import spaces
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor, Qwen2_5_VLForConditionalGeneration
from qwen_vl_utils import process_vision_info
import torch
from PIL import Image
import subprocess
from datetime import datetime
import numpy as np
import os
from gliner import GLiNER
import json
import tempfile
import zipfile

# Initialize GLiNER model
gliner_model = GLiNER.from_pretrained("knowledgator/modern-gliner-bi-large-v1.0")

DEFAULT_NER_LABELS = "person, organization, location, date, event"

# subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)

# models = {
#     "Qwen/Qwen2-VL-7B-Instruct": AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", trust_remote_code=True, torch_dtype="auto", _attn_implementation="flash_attention_2").cuda().eval()

# }

class TextWithMetadata(list):
    def __init__(self, *args, **kwargs):
        super().__init__(*args)
        self.original_text = kwargs.get('original_text', '')
        self.entities = kwargs.get('entities', [])

def array_to_image_path(image_array):
    # Convert numpy array to PIL Image
    img = Image.fromarray(np.uint8(image_array))
    img.thumbnail((1024, 1024))
    
    # Generate a unique filename using timestamp
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    filename = f"image_{timestamp}.png"
    
    # Save the image
    img.save(filename)
    
    # Get the full path of the saved image
    full_path = os.path.abspath(filename)
    
    return full_path
    
models = {
    "Qwen/Qwen2.5-VL-7B-Instruct": Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", trust_remote_code=True, torch_dtype="auto").cuda().eval()

}

processors = {
    "Qwen/Qwen2.5-VL-7B-Instruct": AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", trust_remote_code=True)
}

DESCRIPTION = "This demo uses[Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)"

kwargs = {}
kwargs['torch_dtype'] = torch.bfloat16

user_prompt = '<|user|>\n'
assistant_prompt = '<|assistant|>\n'
prompt_suffix = "<|end|>\n"

@spaces.GPU
def run_example(image, model_id="Qwen/Qwen2.5-VL-7B-Instruct", run_ner=False, ner_labels=DEFAULT_NER_LABELS):
    # First get the OCR text
    text_input = "Convert the image to text."
    image_path = array_to_image_path(image)
    
    model = models[model_id]
    processor = processors[model_id]
    
    prompt = f"{user_prompt}<|image_1|>\n{text_input}{prompt_suffix}{assistant_prompt}"
    image = Image.fromarray(image).convert("RGB")
    messages = [
    {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "image": image_path,
                },
                {"type": "text", "text": text_input},
            ],
        }
    ]
    
    # Preparation for inference
    text = processor.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )
    image_inputs, video_inputs = process_vision_info(messages)
    inputs = processor(
        text=[text],
        images=image_inputs,
        videos=video_inputs,
        padding=True,
        return_tensors="pt",
    )
    inputs = inputs.to("cuda")
    
    # Inference: Generation of the output
    generated_ids = model.generate(**inputs, max_new_tokens=1024)
    generated_ids_trimmed = [
        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
    ]
    output_text = processor.batch_decode(
        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
    )
    
    ocr_text = output_text[0]
    
    # If NER is enabled, process the OCR text
    if run_ner:
        ner_results = gliner_model.predict_entities(
            ocr_text,
            ner_labels.split(","),
            threshold=0.3
        )
        
        # Create a list of tuples (text, label) for highlighting
        highlighted_text = []
        last_end = 0
        
        # Sort entities by start position
        sorted_entities = sorted(ner_results, key=lambda x: x["start"])
        
        # Process each entity and add non-entity text segments
        for entity in sorted_entities:
            # Add non-entity text before the current entity
            if last_end < entity["start"]:
                highlighted_text.append((ocr_text[last_end:entity["start"]], None))
            
            # Add the entity text with its label
            highlighted_text.append((
                ocr_text[entity["start"]:entity["end"]],
                entity["label"]
            ))
            last_end = entity["end"]
        
        # Add any remaining text after the last entity
        if last_end < len(ocr_text):
            highlighted_text.append((ocr_text[last_end:], None))
        
        # Create TextWithMetadata instance with the highlighted text and metadata
        result = TextWithMetadata(highlighted_text, original_text=ocr_text, entities=ner_results)
        return result, result  # Return twice: once for display, once for state
    
    # If NER is disabled, return the text without highlighting
    result = TextWithMetadata([(ocr_text, None)], original_text=ocr_text, entities=[])
    return result, result  # Return twice: once for display, once for state

css = """
  /* Overall app styling */
  .gradio-container {
    max-width: 1200px !important;
    margin: 0 auto;
    padding: 20px;
    background-color: #f8f9fa;
  }

  /* Tabs styling */
  .tabs {
    border-radius: 8px;
    background: white;
    padding: 20px;
    box-shadow: 0 2px 6px rgba(0, 0, 0, 0.1);
  }

  /* Input/Output containers */
  .input-container, .output-container {
    background: white;
    border-radius: 8px;
    padding: 15px;
    margin: 10px 0;
    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
  }

  /* Button styling */
  .submit-btn {
    background-color: #2d31fa !important;
    border: none !important;
    padding: 8px 20px !important;
    border-radius: 6px !important;
    color: white !important;
    transition: all 0.3s ease !important;
  }
  
  .submit-btn:hover {
    background-color: #1f24c7 !important;
    transform: translateY(-1px);
  }

  /* Output text area */
  #output {
    height: 500px;
    overflow: auto;
    border: 1px solid #e0e0e0;
    border-radius: 6px;
    padding: 15px;
    background: #ffffff;
    font-family: 'Arial', sans-serif;
  }

  /* Dropdown styling */
  .gr-dropdown {
    border-radius: 6px !important;
    border: 1px solid #e0e0e0 !important;
  }

  /* Image upload area */
  .gr-image-input {
    border: 2px dashed #ccc;
    border-radius: 8px;
    padding: 20px;
    transition: all 0.3s ease;
  }

  .gr-image-input:hover {
    border-color: #2d31fa;
  }
"""

with gr.Blocks(css=css) as demo:
    # Add state variables to store OCR results
    ocr_state = gr.State()
    
    gr.Image("Caracal.jpg", interactive=False)
    with gr.Tab(label="Image Input", elem_classes="tabs"):
        with gr.Row():
            with gr.Column(elem_classes="input-container"):
                input_img = gr.Image(label="Input Picture", elem_classes="gr-image-input")
                model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value="Qwen/Qwen2.5-VL-7B-Instruct", elem_classes="gr-dropdown")
                
                # Add NER controls
                with gr.Row():
                    ner_checkbox = gr.Checkbox(label="Run Named Entity Recognition", value=False)
                    ner_labels = gr.Textbox(
                        label="NER Labels (comma-separated)", 
                        value=DEFAULT_NER_LABELS,
                        visible=False
                    )
                
                submit_btn = gr.Button(value="Submit", elem_classes="submit-btn")
            with gr.Column(elem_classes="output-container"):
                output_text = gr.HighlightedText(label="Output Text", elem_id="output")

        # Show/hide NER labels based on checkbox
        ner_checkbox.change(
            lambda x: gr.update(visible=x),
            inputs=[ner_checkbox],
            outputs=[ner_labels]
        )
        
        # Modify the submit button click handler to update state
        submit_btn.click(
            run_example,
            inputs=[input_img, model_selector, ner_checkbox, ner_labels],
            outputs=[output_text, ocr_state]  # Add ocr_state to outputs
        )
    with gr.Row():
        filename = gr.Textbox(label="Save filename (without extension)", placeholder="Enter filename to save")
        download_btn = gr.Button("Download Image & Text", elem_classes="submit-btn")
        download_output = gr.File(label="Download")

    # Modify create_zip to use the state data
    def create_zip(image, fname, ocr_result):
        # Validate inputs
        if not fname or image is None:  # Changed the validation check
            return None
        
        try:
            # Convert numpy array to PIL Image if needed
            if isinstance(image, np.ndarray):
                image = Image.fromarray(image)
            elif not isinstance(image, Image.Image):
                return None
            
            with tempfile.TemporaryDirectory() as temp_dir:
                # Save image
                img_path = os.path.join(temp_dir, f"{fname}.png")
                image.save(img_path)
                
                # Use the OCR result from state
                original_text = ocr_result.original_text if ocr_result else ""
                entities = ocr_result.entities if ocr_result else []
                
                # Save text
                txt_path = os.path.join(temp_dir, f"{fname}.txt")
                with open(txt_path, 'w', encoding='utf-8') as f:
                    f.write(original_text)
                
                # Create JSON with text and entities
                json_data = {
                    "text": original_text,
                    "entities": entities,
                    "image_file": f"{fname}.png"
                }
                
                # Save JSON
                json_path = os.path.join(temp_dir, f"{fname}.json")
                with open(json_path, 'w', encoding='utf-8') as f:
                    json.dump(json_data, f, indent=2, ensure_ascii=False)
                
                # Create zip file
                output_dir = "downloads"
                os.makedirs(output_dir, exist_ok=True)
                zip_path = os.path.join(output_dir, f"{fname}.zip")
                
                with zipfile.ZipFile(zip_path, 'w') as zipf:
                    zipf.write(img_path, os.path.basename(img_path))
                    zipf.write(txt_path, os.path.basename(txt_path))
                    zipf.write(json_path, os.path.basename(json_path))
                
                return zip_path

        except Exception as e:
            print(f"Error creating zip: {str(e)}")
            return None

    # Update the download button click handler to include state
    download_btn.click(
        create_zip,
        inputs=[input_img, filename, ocr_state],
        outputs=[download_output]
    )

demo.queue(api_open=False)
demo.launch(debug=True)