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import os
from fastapi import FastAPI, File, UploadFile
from pydantic import BaseModel
from typing import List
import torch
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
from byaldi import RAGMultiModalModel
from PIL import Image
import io
# Initialize FastAPI app
app = FastAPI()
# Define model and processor paths
RAG_MODEL = "vidore/colpali"
QWN_MODEL = "Qwen/Qwen2-VL-7B-Instruct"
QWN_PROCESSOR = "Qwen/Qwen2-VL-2B-Instruct"
# Load models and processors
RAG = RAGMultiModalModel.from_pretrained(RAG_MODEL)
model = Qwen2VLForConditionalGeneration.from_pretrained(
QWN_MODEL,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
device_map="auto",
trust_remote_code=True
).cuda().eval()
processor = AutoProcessor.from_pretrained(QWN_PROCESSOR, trust_remote_code=True)
# Define request model
class DocumentRequest(BaseModel):
text_query: str
# Define processing function
def document_rag(text_query, image):
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": image,
},
{"type": "text", "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("cuda")
generated_ids = model.generate(**inputs, max_new_tokens=50)
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]
# Define API endpoints
@app.post("/process_document")
async def process_document(request: DocumentRequest, file: UploadFile = File(...)):
# Read and process the uploaded file
contents = await file.read()
image = Image.open(io.BytesIO(contents))
# Process the document
result = document_rag(request.text_query, image)
return {"result": result}
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000) |