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Running
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Running
on
Zero
File size: 3,986 Bytes
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import spaces
import gradio as gr
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch
import os
import json
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
model = Qwen2VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2-VL-7B-Instruct",
torch_dtype=torch.bfloat16,
# attn_implementation="flash_attention_2",
device_map="auto",
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
from pydantic import BaseModel
from typing import Tuple
class GeneralRetrievalQuery(BaseModel):
broad_topical_query: str
broad_topical_explanation: str
specific_detail_query: str
specific_detail_explanation: str
visual_element_query: str
visual_element_explanation: str
def get_retrieval_prompt(prompt_name: str) -> Tuple[str, GeneralRetrievalQuery]:
if prompt_name != "general":
raise ValueError("Only 'general' prompt is available in this version")
prompt = """You are an AI assistant specialized in document retrieval tasks. Given an image of a document page, your task is to generate retrieval queries that someone might use to find this document in a large corpus.
Please generate 3 different types of retrieval queries:
1. A broad topical query: This should cover the main subject of the document.
2. A specific detail query: This should focus on a particular fact, figure, or point made in the document.
3. A visual element query: This should reference a chart, graph, image, or other visual component in the document, if present.
Important guidelines:
- Ensure the queries are relevant for retrieval tasks, not just describing the page content.
- Frame the queries as if someone is searching for this document, not asking questions about its content.
- Make the queries diverse and representative of different search strategies.
For each query, also provide a brief explanation of why this query would be effective in retrieving this document.
Format your response as a JSON object with the following structure:
{
"broad_topical_query": "Your query here",
"broad_topical_explanation": "Brief explanation",
"specific_detail_query": "Your query here",
"specific_detail_explanation": "Brief explanation",
"visual_element_query": "Your query here",
"visual_element_explanation": "Brief explanation"
}
If there are no relevant visual elements, replace the third query with another specific detail query.
Here is the document image to analyze:
<image>
Generate the queries based on this image and provide the response in the specified JSON format."""
return prompt, GeneralRetrievalQuery
prompt, pydantic_model = get_retrieval_prompt("general")
@spaces.GPU
def generate_response(image):
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",
)
inputs = inputs.to("cuda")
generated_ids = model.generate(**inputs, max_new_tokens=200)
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:
data = json.loads(output_text[0])
return data
except Exception:
return {}
demo = gr.Interface(fn=generate_response, inputs=gr.Image(type='pil'), outputs="json")
demo.launch()
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