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a95df8d
1
Parent(s):
9f28ec7
vdefrv
Browse files
app.py
CHANGED
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@@ -133,6 +133,8 @@ def search(query: str, ds, images, k):
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results = []
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for idx in top_k_indices:
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results.append((images[idx])) #, f"Page {idx}"
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print("done")
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return results
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@@ -158,7 +160,17 @@ def convert_files(files):
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@spaces.GPU
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def index_gpu(images, ds):
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"""Example script to run inference with ColPali"""
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-
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# run inference - docs
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dataloader = DataLoader(
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images,
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@@ -178,6 +190,9 @@ def index_gpu(images, ds):
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batch_doc = {k: v.to(device) for k, v in batch_doc.items()}
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embeddings_doc = model(**batch_doc)
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ds.extend(list(torch.unbind(embeddings_doc.to("cpu"))))
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return f"Uploaded and converted {len(images)} pages", ds, images
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@spaces.GPU
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results = []
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for idx in top_k_indices:
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results.append((images[idx])) #, f"Page {idx}"
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del model
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del processor
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print("done")
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return results
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@spaces.GPU
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def index_gpu(images, ds):
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"""Example script to run inference with ColPali"""
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# Load colpali model
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model_name = "vidore/colpali-v1.2"
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token = os.environ.get("HF_TOKEN")
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model = ColPali.from_pretrained(
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"vidore/colpaligemma-3b-pt-448-base", torch_dtype=torch.bfloat16, device_map="cuda", token = token).eval()
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model.load_adapter(model_name)
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model = model.eval()
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processor = AutoProcessor.from_pretrained(model_name, token = token)
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mock_image = Image.new("RGB", (448, 448), (255, 255, 255))
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# run inference - docs
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dataloader = DataLoader(
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images,
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batch_doc = {k: v.to(device) for k, v in batch_doc.items()}
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embeddings_doc = model(**batch_doc)
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ds.extend(list(torch.unbind(embeddings_doc.to("cpu"))))
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del model
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del processor
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print("done")
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return f"Uploaded and converted {len(images)} pages", ds, images
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@spaces.GPU
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