Update app.py

app.py

@@ -1,23 +1,10 @@
 import streamlit as st
 import pandas as pd
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-from sklearn.neighbors import NearestNeighbors
-
+f
 # Function to recommend papers
 def recommend_papers(positive_df, unlabelled_df, model="tfidf"):
-    if model == "tfidf":
-        # Use TF-IDF Vectorizer
-        vectorizer = TfidfVectorizer(stop_words='english')
-        positive_matrix = vectorizer.fit_transform(positive_df['abstract'])
-        unlabelled_matrix = vectorizer.transform(unlabelled_df['abstract'])
-    elif model == "nn":
-        # Use Nearest Neighbors
-        nn_model = NearestNeighbors(n_neighbors=5, algorithm='auto')
-        nn_model.fit(positive_df['abstract'])
-        distances, indices = nn_model.kneighbors(unlabelled_df['abstract'])
-        return indices
-
+    pass
+    
 # Streamlit app
 def main():
     st.title("ArXiv Feed Recommendations")