Create app.py

app.py

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+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"],title="Neural Search Engine").launch(share=True)