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import os
import gradio as gr
import torch
from pdf2image import convert_from_path
from PIL import Image
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoProcessor
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
def search(query: str, ds, images):
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"))))
# run evaluation
retriever_evaluator = CustomEvaluator(is_multi_vector=True)
scores = retriever_evaluator.evaluate(qs, ds)
best_page = int(scores.argmax(axis=1).item())
return f"The most relevant page is {best_page}", images[best_page]
def index(file, ds):
"""Example script to run inference with ColPali"""
images = []
for f in file:
images.extend(convert_from_path(f))
# run inference - docs
dataloader = DataLoader(
images,
batch_size=4,
shuffle=False,
collate_fn=lambda x: process_images(processor, x),
)
for batch_doc in tqdm(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 f"Uploaded and converted {len(images)} pages", ds, images
COLORS = ["#4285f4", "#db4437", "#f4b400", "#0f9d58", "#e48ef1"]
# 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="cuda", token=token
).eval()
model.load_adapter(model_name)
processor = AutoProcessor.from_pretrained(model_name, token=token)
device = model.device
mock_image = Image.new("RGB", (448, 448), (255, 255, 255))
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("# ColPali: Efficient Document Retrieval with Vision Language Models 📚🔍")
gr.Markdown("## 1️⃣ Upload PDFs")
file = gr.File(file_types=["pdf"], file_count="multiple")
gr.Markdown("## 2️⃣ Convert the PDFs and upload")
convert_button = gr.Button("🔄 Convert and upload")
message = gr.Textbox("Files not yet uploaded")
embeds = gr.State(value=[])
imgs = gr.State(value=[])
# Define the actions
convert_button.click(index, inputs=[file, embeds], outputs=[message, embeds, imgs])
gr.Markdown("## 3️⃣ Search")
query = gr.Textbox(placeholder="Enter your query here")
search_button = gr.Button("🔍 Search")
message2 = gr.Textbox("Query not yet set")
output_img = gr.Image()
search_button.click(search, inputs=[query, embeds, imgs], outputs=[message2, output_img])
if __name__ == "__main__":
demo.queue(max_size=10).launch(debug=True) |