Spaces:
Paused
Paused
File size: 6,523 Bytes
87b2711 db47c8b 7c99e50 1ca62bc 87b2711 f52050f 7c99e50 f52050f db47c8b ea69d06 6082f24 f52050f 7c99e50 6082f24 f52050f 2fecabe 6082f24 f52050f ec4bd6c f52050f ec4bd6c db47c8b f52050f 40a85e4 f52050f 87b2711 f52050f 40a85e4 f52050f 7c99e50 f52050f 6082f24 f52050f 40a85e4 f52050f 7f5acb9 40a85e4 4a7bfda 40a85e4 4a7bfda 7f5acb9 40a85e4 7f5acb9 40a85e4 db47c8b 7f5acb9 7c99e50 6082f24 7c99e50 6082f24 7f5acb9 4222109 6082f24 7c99e50 6082f24 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 |
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):
# 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
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": image_path, # Use the file path here
},
{"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")
# 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].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]
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": image_path, # Use the file path here
},
{"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_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()
|