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from io import BytesIO
import os
import sys
import tempfile
from fastapi import FastAPI, File, UploadFile
from fastapi.responses import RedirectResponse, StreamingResponse
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
import base64
from reportlab.pdfgen import canvas
from reportlab.lib.pagesizes import letter
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 = []
for idx in top_k_indices:
img_byte_arr = BytesIO()
images[idx].save(img_byte_arr, format='PNG')
img_base64 = base64.b64encode(img_byte_arr.getvalue()).decode('utf-8')
results.append({"image": img_base64, "page": f"Page {idx}"})
# Generate PDF
pdf_buffer = BytesIO()
c = canvas.Canvas(pdf_buffer, pagesize=letter)
width, height = letter
for result in results:
img_base64 = result["image"]
img_data = base64.b64decode(img_base64)
# Create a temporary file to hold the image
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as temp_file:
temp_file.write(img_data)
temp_file.flush()
# Draw the image from the temporary file
c.drawImage(temp_file.name, 0, 0, width, height)
c.showPage()
# Clean up the temporary file
os.remove(temp_file.name)
c.save()
pdf_buffer.seek(0)
# Use StreamingResponse to handle in-memory file
return StreamingResponse(pdf_buffer, media_type='application/pdf', filename='search_results.pdf')
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860) |