Delete app.py

app.py

@@ -1,73 +0,0 @@
-import os
-import pandas as pd
-import streamlit as st
-from langchain_community.vectorstores.faiss import FAISS
-from langchain_huggingface import HuggingFaceEmbeddings
-
-
-st.set_page_config(page_title="ICLR2025 Paper Search", layout="wide")
-os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
-
-
-@st.cache_resource
-def create_vector_store(
-    vector_store_path: str,
-    embedding_model_name: str,
-) -> FAISS:
-    embedding_model = HuggingFaceEmbeddings(model_name=embedding_model_name)
-    vector_store = FAISS.load_local(
-        folder_path=vector_store_path,
-        embeddings=embedding_model,
-        allow_dangerous_deserialization=True,
-    )
-    return vector_store
-
-
-def grab_topk(
-    input_text: str,
-    vector_store: FAISS,
-    top_k: int,
-) -> pd.DataFrame:
-    retriever = vector_store.as_retriever(search_kwargs={"k": top_k + 1})
-    relevant_docs = retriever.get_relevant_documents(input_text)
-
-    abstracts = list()
-    titles = list()
-    urls = list()
-    for relevant_doc in relevant_docs:
-        content = relevant_doc.page_content
-        url = content.split("<BEGIN_URL>")[-1].split("<END_URL>")[0]
-        abstract = content.split("\\n")[-1].split("<BEGIN_URL>")[0]
-        title = content.split("\\n")[0]
-
-        abstracts.append(abstract + "...")
-        titles.append(title)
-        urls.append(url)
-    return pd.DataFrame({"title": titles, "abstract": abstracts, "url": urls})
-
-
-if __name__ == "__main__":
-    vector_store_path = "db"
-    embedding_model_name = "intfloat/multilingual-e5-large-instruct"
-    vector_store = create_vector_store(
-        vector_store_path,
-        embedding_model_name,
-    )
-
-    st.markdown("## ICLR2025")
-    st.markdown("- list of papers (https://iclr.cc/Downloads/2025)")
-    input_text = st.text_input(
-        "query",
-        "",
-        placeholder="Enter the keywords you are interested in...",
-    )
-    top_k = st.number_input("top_k", min_value=1, value=10, step=1)
-
-    if st.button("検索"):
-        stripped_input_text = input_text.strip()
-        df = grab_topk(
-            stripped_input_text,
-            vector_store,
-            top_k,
-        )
-        st.table(df)