Delete app.py

app.py

@@ -1,109 +0,0 @@
-# -*- 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()
-