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README.md

@@ -1,10 +1,10 @@
 ---
-title: Hack Qa
-emoji: 🐢
-colorFrom: yellow
-colorTo: yellow
+title: Wiki Chat
+emoji: 👀
+colorFrom: blue
+colorTo: red
 sdk: gradio
-sdk_version: 3.17.0
+sdk_version: 3.16.2
 app_file: app.py
 pinned: false
 ---

app.py

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+# -*- coding: utf-8 -*-
+"""wiki_chat.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1P5rJeCXRSsDJw_1ksnHmodH6ng2Ot5NW
+"""
+
+# !pip install gradio
+
+# !pip install -U sentence-transformers
+
+# !pip install datasets
+
+
+import gradio as gr
+from sentence_transformers import SentenceTransformer, CrossEncoder, util
+from torch import tensor as torch_tensor
+from datasets import load_dataset
+
+"""# import models"""
+
+bi_encoder = SentenceTransformer('multi-qa-MiniLM-L6-cos-v1')
+bi_encoder.max_seq_length = 256     #Truncate long passages to 256 tokens
+
+#The bi-encoder will retrieve top_k documents. We use a cross-encoder, to re-rank the results list to improve the quality
+cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+
+"""# import datasets"""
+dataset = load_dataset("gfhayworth/wiki_mini", split='train')
+mypassages = list(dataset.to_pandas()['psg'])
+
+dataset_embed = load_dataset("gfhayworth/wiki_mini_embed", split='train')
+dataset_embed_pd = dataset_embed.to_pandas()
+mycorpus_embeddings = torch_tensor(dataset_embed_pd.values)
+
+def search(query, top_k=20, top_n = 1):
+  question_embedding = bi_encoder.encode(query, convert_to_tensor=True)
+  question_embedding = question_embedding #.cuda()
+  hits = util.semantic_search(question_embedding, mycorpus_embeddings, top_k=top_k)
+  hits = hits[0]  # Get the hits for the first query
+
+  ##### Re-Ranking #####
+  cross_inp = [[query, mypassages[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)
+  predictions = hits[:top_n]
+  return predictions
+  # for hit in hits[0:3]:
+  #     print("\t{:.3f}\t{}".format(hit['cross-score'], mypassages[hit['corpus_id']].replace("\n", " ")))
+
+def get_text(qry):
+  predictions = search(qry)
+  prediction_text = []
+  for hit in predictions:
+    prediction_text.append("{}".format(mypassages[hit['corpus_id']]))
+  return prediction_text
+
+# def prt_rslt(qry):
+#   rslt = get_text(qry)
+#   for r in rslt:
+#     print(r)
+
+# prt_rslt("who is the best rapper in the world?")
+
+"""# chat example"""
+
+def chat(message, history):
+  history = history or []
+  message = message.lower()
+  
+  responses = get_text(message)
+  for response in responses:
+    history.append((message, response))
+  return history, history
+
+css=".gradio-container {background-color: lightgray}"
+
+with gr.Blocks(css=css) as demo:
+  history_state = gr.State()
+  gr.Markdown('# WikiBot')
+  title='Wikipedia Chatbot'
+  description='chatbot with search on Wikipedia'
+  with gr.Row():
+    chatbot = gr.Chatbot()
+  with gr.Row():
+    message = gr.Textbox(label='Input your question here:',
+                         placeholder='How many countries are in Europe?',
+                         lines=1)
+    submit = gr.Button(value='Send',
+                       variant='secondary').style(full_width=False)
+  submit.click(chat,
+               inputs=[message, history_state],
+               outputs=[chatbot, history_state])
+  gr.Examples(
+            examples=["How many countries are in Europe?",
+                      "Was Roman Emperor Constantine I a Christian?",
+                      "Who is the best rapper in the world?"],
+            inputs=message
+        )
+
+demo.launch()
+

requirements.txt

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+sentence-transformers
+datasets