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import os
import json
from PIL import Image
import torch
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
# Initialize Qwen2.5-VL model
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2.5-VL-7B-Instruct",
torch_dtype=torch.bfloat16,
device_map="cuda"
#attn_implementation="flash_attention_2"
)
min_pixels = 256 * 28 * 28
max_pixels = 1080 * 28 * 28
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
# Initialize Pix2Struct OCR model
ocr_processor = Pix2StructProcessor.from_pretrained("google/pix2struct-textcaps-base")
ocr_model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-textcaps-base")
# Load prompt
def load_prompt():
with open("prompts/prompt.txt", "r") as f:
return f.read()
# Try extracting JSON from text
def try_extract_json(text):
try:
return json.loads(text)
except json.JSONDecodeError:
start = text.find('{')
if start == -1:
return text
brace_count = 0
json_candidate = ''
for i in range(start, len(text)):
if text[i] == '{':
brace_count += 1
elif text[i] == '}':
brace_count -= 1
json_candidate += text[i]
if brace_count == 0:
break
try:
return json.loads(json_candidate)
except json.JSONDecodeError:
return text
# Extract OCR text using Pix2Struct
def extract_all_text_pix2struct(image: Image.Image):
inputs = ocr_processor(images=image, return_tensors="pt")
predictions = ocr_model.generate(**inputs, max_new_tokens=512)
output_text = ocr_processor.decode(predictions[0], skip_special_tokens=True)
return output_text.strip()
# Assign event/gateway names from OCR text
def assign_event_gateway_names_from_ocr(json_data: dict, ocr_text: str):
if not ocr_text or not json_data:
return json_data
lines = [line.strip() for line in ocr_text.split('\n') if line.strip()]
def assign_best_guess(obj):
if not obj.get("name") or obj["name"].strip() == "":
obj["name"] = "(label unknown)"
for evt in json_data.get("events", []):
assign_best_guess(evt)
for gw in json_data.get("gateways", []):
assign_best_guess(gw)
return json_data
# Run model
def run_model(image: Image.Image):
prompt = load_prompt()
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": image},
{"type": "text", "text": prompt}
]
}
]
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"
).to("cuda")
generated_ids = model.generate(**inputs, max_new_tokens=5000)
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
)[0]
parsed_json = try_extract_json(output_text)
# Apply OCR post-processing
ocr_text = extract_all_text_pix2struct(image)
parsed_json = assign_event_gateway_names_from_ocr(parsed_json, ocr_text)
return {
"json": parsed_json,
"raw": output_text
} |