initial commit (v1.2)

README.md

@@ -1,8 +1,8 @@
 ---
 title: ColPali Transformers
-emoji: 🌖
+emoji: 📚
 colorFrom: yellow
-colorTo: yellow
+colorTo: red
 sdk: gradio
 sdk_version: 5.9.1
 app_file: app.py

app.py

@@ -0,0 +1,141 @@
+import os
+
+import gradio as gr
+import spaces
+import torch
+from pdf2image import convert_from_path
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from transformers import ColPaliForRetrieval, ColPaliProcessor
+
+
+@spaces.GPU
+def install_fa2():
+    print("Install FA2")
+    os.system("pip install flash-attn --no-build-isolation")
+
+
+# install_fa2()
+
+model_name = "vidore/colpali-v1.2-hf"
+
+
+model = ColPaliForRetrieval.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="cuda:0",  # or "mps" if on Apple Silicon
+    # attn_implementation="flash_attention_2", # should work on A100
+).eval()
+processor = ColPaliProcessor.from_pretrained(model_name)
+
+
+@spaces.GPU
+def search(query: str, ds, images, k):
+    k = min(k, len(ds))
+    device = "cuda:0" if torch.cuda.is_available() else "cpu"
+    if device != model.device:
+        model.to(device)
+
+    qs = []
+    with torch.no_grad():
+        batch_query = processor(text=[query]).to(model.device)
+        query_embeddings = model(**batch_query).embeddings
+        qs.extend(list(torch.unbind(query_embeddings.to("cpu"))))
+
+    scores = processor.score_retrieval(qs, ds)
+
+    top_k_indices = scores[0].topk(k).indices.tolist()
+
+    results = []
+    for idx in top_k_indices:
+        results.append((images[idx], f"Page {idx}"))
+
+    return results
+
+
+def index(files, ds):
+    print("Converting files")
+    images = convert_files(files)
+    print(f"Files converted with {len(images)} images.")
+    return index_gpu(images, ds)
+
+
+def convert_files(files):
+    images = []
+    for f in files:
+        images.extend(convert_from_path(f, thread_count=4))
+
+    if len(images) >= 150:
+        raise gr.Error("The number of images in the dataset should be less than 150.")
+    return images
+
+
+@spaces.GPU
+def index_gpu(images, ds):
+    """Example script to run inference with ColPali"""
+
+    device = "cuda:0" if torch.cuda.is_available() else "cpu"
+    if device != model.device:
+        model.to(device)
+
+    # run inference - docs
+    dataloader = DataLoader(
+        images,
+        batch_size=4,
+        shuffle=False,
+        collate_fn=lambda x: processor(images=x).to(model.device),
+    )
+
+    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).embeddings
+        ds.extend(list(torch.unbind(embeddings_doc.to("cpu"))))
+    return f"Uploaded and converted {len(images)} pages", ds, images
+
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        "# ColPali: Efficient Document Retrieval with Vision Language Models 📚"
+    )
+    gr.Markdown("""Demo to test the Transformers 🤗 implementation of ColPali on PDF documents.<br>
+    ColPali is the model implemented from the [ColPali paper](https://arxiv.org/abs/2407.01449).<br>
+    This demo allows you to upload PDF files and search for the most relevant pages based on your query.
+    Refresh the page if you change documents!<br>
+    ⚠️ This demo uses a model trained exclusively on A4 PDFs in portrait mode, containing english text. Performance is expected to drop for other page formats and languages.<br>
+    Other models will be released with better robustness towards different languages and document formats!
+    Demo by [manu](https://huggingface.co/spaces/manu/ColPali-demo)
+    """)
+    with gr.Row():
+        with gr.Column(scale=2):
+            gr.Markdown("## 1️⃣ Upload PDFs")
+            file = gr.File(
+                file_types=["pdf"], file_count="multiple", label="Upload PDFs"
+            )
+
+            convert_button = gr.Button("🔄 Index documents")
+            message = gr.Textbox("Files not yet uploaded", label="Status")
+            embeds = gr.State(value=[])
+            imgs = gr.State(value=[])
+
+        with gr.Column(scale=3):
+            gr.Markdown("## 2️⃣ Search")
+            query = gr.Textbox(placeholder="Enter your query here", label="Query")
+            k = gr.Slider(
+                minimum=1, maximum=10, step=1, label="Number of results", value=5
+            )
+
+    # Define the actions
+    search_button = gr.Button("🔍 Search", variant="primary")
+    output_gallery = gr.Gallery(
+        label="Retrieved Documents", height=600, show_label=True
+    )
+
+    convert_button.click(index, inputs=[file, embeds], outputs=[message, embeds, imgs])
+    search_button.click(
+        search, inputs=[query, embeds, imgs, k], outputs=[output_gallery]
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=10).launch(debug=True)

packages.txt

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+poppler-utils

requirements.txt

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+torch
+git+https://github.com/huggingface/transformers.git@main
+pdf2image