Update app.py

app.py

@@ -3,10 +3,12 @@ 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()
@@ -16,16 +18,24 @@ def search(query,top_k=100):
     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)
+
+inp=gr.inputs.Textbox(lines=1, placeholder=None, default="", label="search you query here")
+out=gr.outputs.Textbox(type="auto",label="search results")
+
+iface = gr.Interface(fn=search, inputs=inp, outputs=[out,out,out,out,out],title="Neural Search Engine",theme="huggingface",layout='vertical')
+iface.launch()