import re
import gradio as gr

from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch
from ast import literal_eval

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

# default processer
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)



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': '',                                               
            'Amount': ''
                            }
'''

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_name, prompt):
    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "image": image_name,
                },
                {"type": "text", "text":  prompt},
            ],
        }
    ]

    # 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=1500)
    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
    )
    try:
        # almost_json = output_text[0].replace('```\n', '').replace('\n```', '')
        almost_json = output_text[0].split('```\n')[-1].split('\n```')[0]
 
        json = literal_eval(almost_json)
    except:
        try:
            # almost_json = output_text[0].replace('```json\n', '').replace('\n```', '')
            almost_json = output_text[0].split('```json\n')[-1].split('\n```')[0]
            json = literal_eval(almost_json)
        except:
            json = output_text[0]
    return json

def process_document(image):
    one = demo(image, other_benifits)
    two = demo(image, tax_deductions)
    json_op = {
        "tax_deductions": one,
        "other_benifits": two
              }
    return json_op

# article = "<p style='text-align: center'><a href='https://www.xelpmoc.in/' target='_blank'>Made by Xelpmoc</a></p>"

demo = gr.Interface(
    fn=process_document,
    inputs="image",
    outputs="json",
    title="PaySlip_Demo_Model",
    # article=article,
    enable_queue=True,
    examples=[["Slip_1.jpg"], ["Slip_2.jpg"]],
    cache_examples=False)

demo.launch()