removed some cruft from app.py

app.py

@@ -1,4 +1,4 @@
-from fastapi import FastAPI, Request, Query
+from fastapi import FastAPI, Query
 from fastapi.responses import JSONResponse
 from src.embeddings_search import create_embeddings_search_function_from_embeddings_df
 from src.tfidf_search import create_tfidf_search_function
@@ -13,52 +13,28 @@ path_prefix = "/Users/wes/Google Drive/Shared drives/datalab/projects/2025_coul_
 block_embeddings_df_path = "block_embeddings/block-embeddings.parquet"
 doc_tfidf_df_path = "block_tfidf/TF-IDF-doc-text.parquet"
 tfidf_vectorizer_path = "block_tfidf/tfidf_vectorizer_doc_text.joblib"
-model_name = "sentence-transformers/all-MiniLM-L6-v2"
+
+sbert_query_docs = create_embeddings_search_function_from_embeddings_df(
+    model_name = "sentence-transformers/all-MiniLM-L6-v2",
+    embeddings_df_path = block_embeddings_df_path,
+    device = "cpu")
+tfidf_query_docs = create_tfidf_search_function(
+    dtm_df_path = doc_tfidf_df_path,
+    vectorizer_path = tfidf_vectorizer_path,
+    model_name = "facebook/fasttext-en-vectors")
+
+print("generated the search functions!")
 
 app = FastAPI()
 
 
 @app.get("/")
 def default():
-
     return {"status": "ok", "version": 0.1}
 
 
-@app.get("/sbert")
-def sb(query: str):
-    res_sbert = sbert_query_docs(query)
-
-    return {"scores": str(res_sbert)}
-
-
-@app.get("/tfidf")
-def tf(query: str):
-    res_tfidf = query_docs(query)
-
-    return {"scores": str(res_tfidf)}
-
-
-@app.get("/square")
-def square(x: int):
-    return {"result": x * x}
-
-
 @app.get("/search", response_class=JSONResponse)
 def search(q: str = Query(..., description="Search query")):
-    block_embeddings_df_path = "block_embeddings/block-embeddings.parquet"
-    doc_tfidf_df_path = "block_tfidf/TF-IDF-doc-text.parquet"
-    tfidf_vectorizer_path = "block_tfidf/tfidf_vectorizer_doc_text.joblib"
-
-    sbert_query_docs = create_embeddings_search_function_from_embeddings_df(
-        model_name = "sentence-transformers/all-MiniLM-L6-v2",
-        embeddings_df_path = block_embeddings_df_path,
-        device = "cpu")
-    tfidf_query_docs = create_tfidf_search_function(
-        dtm_df_path = doc_tfidf_df_path,
-        vectorizer_path = tfidf_vectorizer_path,
-        model_name = "facebook/fasttext-en-vectors")
-
-
     res_tfidf = tfidf_query_docs(q)
     res_sbert = sbert_query_docs(q)