app.py

import os
import sys

from fastapi import FastAPI, File, UploadFile
from fastapi.responses import RedirectResponse
import gradio as gr
import requests
import uvicorn
from typing import List
import torch
from pdf2image import convert_from_bytes
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.get("/")
def read_root():
    return RedirectResponse(url="/docs")

@app.post("/index")
async def index(files: List[UploadFile] = File(...)):
    global ds, images
    images = []
    ds = []
    for file in files:
        content = await file.read()
        pdf_image_list = convert_from_bytes(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")
async 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}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)