Create app.py

app.py

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+import streamlit as st
+import torch
+import pandas as pd
+from sentence_transformers import SentenceTransformer, util
+import pickle
+
+# Load data
+data = pd.read_csv("/content/arxiv_data.csv")
+titles = data["titles"]
+
+# Load pre-trained SentenceTransformer model
+model = SentenceTransformer("all-MiniLM-L6-v2")
+
+# Load saved embeddings
+with open("/content/embedding.pkl", "rb") as f:
+    Lencode = pickle.load(f)
+
+# Load saved model
+with open("/content/ModelRec.pkl", "rb") as f:
+    lModelRec = pickle.load(f)
+
+def recomm(inputPaper):
+    encodePaper = lModelRec.encode(inputPaper)
+    cosine_score = util.cos_sim(Lencode, encodePaper)
+    top_scores = torch.topk(cosine_score, dim=0, k=4)
+    paperList = []
+    for i in top_scores.indices:
+        paperList.append(titles[i.item()])
+    return paperList
+
+# Streamlit UI
+st.title("Paper Recommendation System")
+
+input_paper = st.text_input("Enter the name of the paper")
+
+if st.button("Recommend"):
+    recommended_papers = recomm(input_paper)
+    st.write("Recommended Papers:")
+    for paper in recommended_papers:
+        st.write(paper)