Delete app.py

app.py

@@ -1,31 +0,0 @@
-from sentence_transformers import SentenceTransformer, CrossEncoder, util
-import torch
-import pickle
-import pandas as pd
-import gradio as gr
-bi_encoder = SentenceTransformer("multi-qa-MiniLM-L6-cos-v1")
-cross_encoder = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2")
-corpus_embeddings=pd.read_pickle("corpus_embeddings_cpu.pkl")
-corpus=pd.read_pickle("corpus.pkl")
-def search(query,top_k=100):
-    print("Top 5 Answer by the NSE:")
-    print()
-    ans=[]
-    ##### Sematic Search #####
-    # Encode the query using the bi-encoder and find potentially relevant passages
-    question_embedding = bi_encoder.encode(query, convert_to_tensor=True)
-    hits = util.semantic_search(question_embedding, corpus_embeddings, top_k=top_k)
-    hits = hits[0]  # Get the hits for the first query
-    ##### Re-Ranking #####
-    # Now, score all retrieved passages with the cross_encoder
-    cross_inp = [[query, corpus[hit['corpus_id']]] for hit in hits]
-    cross_scores = cross_encoder.predict(cross_inp)
-    # Sort results by the cross-encoder scores
-    for idx in range(len(cross_scores)):
-        hits[idx]['cross-score'] = cross_scores[idx]
-    hits = sorted(hits, key=lambda x: x['cross-score'], reverse=True)
-    
-    for idx, hit in enumerate(hits[0:5]):
-        ans.append(corpus[hit['corpus_id']])
-    return ans[0],ans[1],ans[2],ans[3],ans[4]
-iface = gr.Interface(fn=search, inputs=["text"], outputs=["textbox","textbox","textbox","textbox","textbox"]).launch(share=True)