Adding app

app.py

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+import faiss
+import gradio as gr
+import numpy as np
+import pandas as pd
+import torch.nn.functional as F
+from sentence_transformers import SentenceTransformer
+
+DIM = 768
+model = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True)
+
+papers_df = pd.read_csv("data/cvpr2024_papers_with_details.csv", index=None, on_bad_lines='skip')
+papers_df = papers_df[~papers_df["summary"].isna() & ~papers_df["pdf_path"].isna()]
+
+with open('data/embeddings.npy', 'rb') as f:
+    embeddings = np.load(f)
+
+index = faiss.IndexFlatL2(DIM)
+index.add(embeddings)
+
+
+def encode_query(query):
+    query_embeddings = model.encode([query], convert_to_tensor=True)
+    query_embeddings = F.layer_norm(embeddings, normalized_shape=(embeddings.shape[1],))
+    query_embeddings = embeddings[:, :DIM]
+    query_embeddings = F.normalize(embeddings, p=2, dim=1)
+    return query_embeddings
+
+def search_nearest_papers(query, k=5):
+    query_embeddings = encode_query(query)
+    D, I = index.search(query_embeddings.numpy(), k)
+    return papers_df.iloc[I[0]][["title", "summary", "pdf_path"]]
+
+demo = gr.Interface(
+    search_nearest_papers,
+    [
+        "text",
+        gr.inputs.Slider(1, 10, default=5),
+    ],
+    "dataframe",
+)
+
+if __name__ == "__main__":
+    demo.launch()

data/embeddings.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:939ae59573f4807a026ca5eeaa9bafc6daa3cbb6a7fb9b1f163991c6ede3ac09
+size 3200

requirements.txt

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+faiss-cpu
+sentence-transformers
+einops
+pandas
+numpy