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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
# 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 process_document(image):
with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp_file:
image = Image.fromarray(image)
image.save(tmp_file.name)
image_path = tmp_file.name
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": image_path,
},
{"type": "text", "text": '''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':""}}},
}'''},
],
}
]
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")
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].split('```\n')[-1].split('\n```')[0]
json = literal_eval(almost_json)
except:
try:
almost_json = output_text[0].split('```json\n')[-1].split('\n```')[0]
json = literal_eval(almost_json)
except:
json = output_text[0]
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": image_path,
},
{"type": "text", "text": '''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'
}'''},
],
}
]
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_2 = output_text[0].split('```\n')[-1].split('\n```')[0]
json_2 = literal_eval(almost_json_2)
except:
try:
almost_json_2 = output_text[0].split('```json\n')[-1].split('\n```')[0]
json_2 = literal_eval(almost_json_2)
except:
json_2 = output_text[0]
# json_op = {
# "tax_deductions": json,
# "other_benifits": json_2
# }
# # Optionally, you can delete the temporary file after use
os.remove(image_path)
return json, json_2
# Create Gradio interface
demo = gr.Interface(
fn=process_document,
inputs="image", # Gradio will handle the image input
outputs=[
gr.outputs.JSON(label="Tax Deductions Information"), # First output box with heading
gr.outputs.JSON(label="Other Benefits and Information") # Second output box with heading
],
title="PaySlip_Demo_Model",
examples=[["Slip_1.jpg"], ["Slip_2.jpg"]],
cache_examples=False
)
demo.launch()
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