app.py

import os
import tempfile
import gradio as gr
import numpy as np
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch
from ast import literal_eval
from PIL import Image

import logging
logging.basicConfig(level=logging.INFO)

# Load the model on the available device(s)
model = Qwen2VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen2-VL-7B-Instruct", torch_dtype="auto", device_map="auto"
)

# Load the processor
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")

# Define your prompts
other_benifits = '''Extract the following information in the given format:
        {'other_benefits_and_information': {
            '401k eru: {'This Period':'', 'Year-to-Date':''}},
            'quota summary':
                            {
                            'sick:': '',
                            'vacation:': '',
                            }
            'payment method': 'eg. Direct payment',
            'Amount': 'eg. 12.99'
                            }
'''

tax_deductions = '''Extract the following information in the given format:
                {
                'tax_deductions': {
                    'federal:': {
                        'withholding tax:': {'Amount':'', 'Year-To_Date':""},
                        'ee social security tax:': {'Amount':'', 'Year-To_Date':""},
                        'ee medicare tax:': {'Amount':'', 'Year-To_Date':""}},
                    'california:': {
                        'withholding tax:': {'Amount':'', 'Year-To_Date':""},
                        'ee disability tax:': {'Amount':'', 'Year-To-Date':""}}},
                }
'''

def demo(image_path, prompt):
    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "image": image_path,  # Use the file path here
                },
                {"type": "text", "text":  prompt},
            ],
        }
    ]
    logging.info("Step 1: Preparing inference")
    # Preparation for inference
    text = processor.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )
    logging.info("2")
    image_inputs, video_inputs = process_vision_info(messages)
    logging.info("3")
    inputs = processor(
        text=[text],
        images=image_inputs,
        videos=video_inputs,
        padding=True,
        return_tensors="pt",
    )
    logging.info("4")
    inputs = inputs.to("cuda")
    logging.info("5")
    # Inference: Generation of the output
    generated_ids = model.generate(**inputs, max_new_tokens=1500)
    logging.info("6")
    generated_ids_trimmed = [
        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
    ]
    logging.info("7")
    output_text = processor.batch_decode(
        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
    )
    logging.info("8", output_text)

    # Handle output text to convert it into JSON
    try:
        almost_json = output_text[0].split('\n')[-1].split('\n')[0]
        json = literal_eval(almost_json)
    except:
        json = output_text[0]  # Return raw output if JSON parsing fails
    return json

def process_document(image):
    # Save the uploaded image to a temporary file
    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp_file:
        image = Image.fromarray(image)  # Convert NumPy array to PIL Image
        image.save(tmp_file.name)  # Save the image to the temporary file
        image_path = tmp_file.name  # Get the path of the saved file
    logging.info("the path made for image: %s", image_path)
    # Process the image with your model
    one = demo(image_path, other_benifits)
    logging.info("kjf")
    two = demo(image_path, tax_deductions)
    json_op = {
        "tax_deductions": one,
        "other_benifits": two
    }

    # Optionally, you can delete the temporary file after use
    os.remove(image_path)

    return json_op

# Create Gradio interface
demo = gr.Interface(
    fn=process_document,
    inputs="image",  # Gradio will handle the image input
    outputs="json",
    title="PaySlip_Demo_Model",
    examples=[["Slip_1.jpg"], ["Slip_2.jpg"]],
    cache_examples=False
)

demo.launch()