Upload app.py

app.py

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+from sentence_transformers import SentenceTransformer, CrossEncoder, util
+import torch
+import pickle
+import pandas as pd
+
+
+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]
+
+
+exp = ["Who is steve jobs?", "What is coldplay?", "What is a turing test?",
+       "What is the most interesting thing about our universe?", "What are the most beautiful places on earth?"]
+
+desc = "This is a semantic search engine powered by SentenceTransformers (Nils_Reimers) with a retrieval and reranking system on Wikipedia corous. This will return the top 5 results. So Quest on with Transformers."
+
+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], examples=exp, article=desc,
+                     title="Search Engine", theme="huggingface", layout='vertical')
+iface.launch()