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import io
import os
import sys
from fastapi import FastAPI, File, UploadFile
from fastapi.responses import RedirectResponse
import gradio as gr
import requests
from typing import List
import torch
from pdf2image import convert_from_path
from PIL import Image
from torch.utils.data import DataLoader
from transformers import AutoProcessor
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), './colpali-main')))
from colpali_engine.models.paligemma_colbert_architecture import ColPali
from colpali_engine.trainer.retrieval_evaluator import CustomEvaluator
from colpali_engine.utils.colpali_processing_utils import (
process_images,
process_queries,
)
app = FastAPI()
# Load model
model_name = "vidore/colpali"
token = os.environ.get("HF_TOKEN")
model = ColPali.from_pretrained(
"google/paligemma-3b-mix-448", torch_dtype=torch.bfloat16, device_map="cpu", token = token).eval()
model.load_adapter(model_name)
processor = AutoProcessor.from_pretrained(model_name, token = token)
device = "cuda:0" if torch.cuda.is_available() else "cpu"
if device != model.device:
model.to(device)
mock_image = Image.new("RGB", (448, 448), (255, 255, 255))
# In-memory storage
ds = []
images = []
@app.post("/index")
def index(files: List[UploadFile] = File(...)):
global ds, images
images = []
ds = []
for file in files:
content = file.read()
pdf_image_list = convert_from_path(io.BytesIO(content))
images.extend(pdf_image_list)
dataloader = DataLoader(
images,
batch_size=4,
shuffle=False,
collate_fn=lambda x: process_images(processor, x),
)
for batch_doc in dataloader:
with torch.no_grad():
batch_doc = {k: v.to(device) for k, v in batch_doc.items()}
embeddings_doc = model(**batch_doc)
ds.extend(list(torch.unbind(embeddings_doc.to("cpu"))))
return {"message": f"Uploaded and converted {len(images)} pages"}
@app.post("/search")
def search(query: str, k: int):
qs = []
with torch.no_grad():
batch_query = process_queries(processor, [query], mock_image)
batch_query = {k: v.to(device) for k, v in batch_query.items()}
embeddings_query = model(**batch_query)
qs.extend(list(torch.unbind(embeddings_query.to("cpu"))))
retriever_evaluator = CustomEvaluator(is_multi_vector=True)
scores = retriever_evaluator.evaluate(qs, ds)
top_k_indices = scores.argsort(axis=1)[0][-k:][::-1]
results = [{"page": idx, "image": "image_placeholder"} for idx in top_k_indices]
return {"results": results}
# Rediriger la racine vers /docs
@app.get("/")
def read_root():
return RedirectResponse(url="/docs") |