Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,181 Bytes
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import subprocess
import uuid
import gradio as gr
import spaces
import torch
from qwen_vl_utils import process_vision_info
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
from voyager_index import Voyager
subprocess.run(
"pip install flash-attn --no-build-isolation",
env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
shell=True,
)
device = "cuda" if torch.cuda.is_available() else "cpu"
# Initialize the model and processor
model = (
Qwen2VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True, torch_dtype=torch.bfloat16
)
.to(device)
.eval()
)
processor = AutoProcessor.from_pretrained(
"Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True
)
def create_index(session_id):
return Voyager(embedding_size=1536, override=True, index_name=f"{session_id}")
def add_to_index(files, index):
index.add_documents([file.name for file in files], batch_size=1)
return f"Added {len(files)} files to the index."
@spaces.GPU
def query_index(query, index):
res = index(query, k=1)
retrieved_image = res["documents"][0][0]["image"]
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": retrieved_image,
},
{"type": "text", "text": query},
],
}
]
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(device)
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,
)
return output_text[0], retrieved_image
# Define the Gradio interface
with gr.Blocks() as demo:
session_id = gr.State(lambda: str(uuid.uuid4()))
index = gr.State(lambda: create_index(session_id.value))
gr.Markdown("# Full vision pipeline demo")
with gr.Tab("Add to Index"):
file_input = gr.File(file_count="multiple", label="Upload Files")
add_button = gr.Button("Add to Index")
add_output = gr.Textbox(label="Result")
add_button.click(add_to_index, inputs=[file_input, index], outputs=add_output)
with gr.Tab("Query Index"):
query_input = gr.Textbox(label="Enter your query")
query_button = gr.Button("Submit Query")
with gr.Row():
query_output = gr.Textbox(label="Answer")
image_output = gr.Image(label="Retrieved Image")
query_button.click(
query_index,
inputs=[query_input, index],
outputs=[query_output, image_output],
)
# Launch the interface
demo.launch()
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