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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_function(image_path, prompt):
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": image_path, # Use the file path here
},
{"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
)
# 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:
try:
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):
# 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
# Process the image with your model
one = process_function(image_path, other_benifits)
two = process_function(image_path, tax_deductions)
# Optionally, you can delete the temporary file after use
os.remove(image_path)
return one, two
# 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.JSON(label="Tax Deductions Information"), # First output box with heading
gr.JSON(label="Other Benefits and Information") # Second output box with heading
],
title="<div style='text-align: center;'>Information Extraction From PaySlip</div>",
examples=[["Slip_1.jpg"], ["Slip_2.jpg"]],
cache_examples=False
)
demo.launch()
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