app.py

import gradio as gr
import spaces
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
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

# 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()

# }
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-VL-7B-Instruct": Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", trust_remote_code=True, torch_dtype="auto").cuda().eval()

}

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

DESCRIPTION = "This demo uses[Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-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-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
        )
        
        # Format entities for highlighting
        entities = []
        for entity in ner_results:
            entities.append((
                entity["start"],
                entity["end"],
                entity["label"]
            ))
        
        return [(ocr_text, entities)]
    
    return [(ocr_text, [])]

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:
    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-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]
        )
        
        # Update submit button click handler
        submit_btn.click(
            run_example,
            inputs=[input_img, model_selector, ner_checkbox, ner_labels],
            outputs=[output_text]
        )
    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")

    def create_zip(image, text_data, fname):
        if not isinstance(image, (Image.Image, np.ndarray)) or not text_data or not fname:
            return None
        
        # Convert numpy array to PIL Image if needed
        if isinstance(image, np.ndarray):
            image = Image.fromarray(image)
        
        # Create a temporary directory
        with tempfile.TemporaryDirectory() as temp_dir:
            # Save image
            img_ext = image.format.lower() if hasattr(image, 'format') else 'png'
            img_path = os.path.join(temp_dir, f"{fname}.{img_ext}")
            image.save(img_path)
            
            # Extract text and entities from the HighlightedText output
            text, entities = text_data[0] if isinstance(text_data, list) else (text_data, [])
            
            # Save text
            txt_path = os.path.join(temp_dir, f"{fname}.txt")
            with open(txt_path, 'w', encoding='utf-8') as f:
                f.write(text)
            
            # Create JSON with text, entities, and image info
            json_data = {
                "text": text,
                "entities": [
                    {
                        "start": start,
                        "end": end,
                        "label": label,
                        "text": text[start:end]
                    }
                    for start, end, label in entities
                ],
                "image_file": f"{fname}.{img_ext}"
            }
            
            # 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
            zip_path = os.path.join(temp_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

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