Upload app.py

replace to use LLM and new data sources

app.py

@@ -0,0 +1,162 @@
+# -*- coding: utf-8 -*-
+"""wiki_chat_3_hack.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1chXsWeq1LzbvYIs6H73gibYmNDRbIgkD
+"""
+
+#!pip install gradio
+
+#!pip install -U sentence-transformers
+
+#!pip install datasets
+
+#!pip install langchain
+
+#!pip install openai
+
+#!pip install faiss-cpu
+
+#import numpy as np
+import gradio as gr
+#import random
+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/hack_policy", split='train')
+mypassages = list(dataset.to_pandas()['psg'])
+
+dataset_embed = load_dataset("gfhayworth/hack_policy_embed", split='train')
+dataset_embed_pd = dataset_embed.to_pandas()
+mycorpus_embeddings = torch_tensor(dataset_embed_pd.values)
+
+def search(query, passages = mypassages, doc_embedding = mycorpus_embeddings, 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, doc_embedding, top_k=top_k)
+  hits = hits[0]  # Get the hits for the first query
+
+  ##### Re-Ranking #####
+  cross_inp = [[query, passages[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("What is the name of the plan described by this summary of benefits?")
+
+"""# new LLM based functions"""
+
+import os
+os.environ["OPENAI_API_KEY"] = "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"
+
+from langchain.llms import OpenAI
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.embeddings import HuggingFaceEmbeddings
+
+from langchain.text_splitter import CharacterTextSplitter
+#from langchain.vectorstores.faiss import FAISS
+from langchain.docstore.document import Document
+from langchain.prompts import PromptTemplate
+from langchain.chains.question_answering import load_qa_chain
+from langchain.chains.qa_with_sources import load_qa_with_sources_chain
+from langchain.chains import VectorDBQAWithSourcesChain
+
+chain_qa = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="stuff")
+
+def get_text_fmt(qry, passages = mypassages, doc_embedding=mycorpus_embeddings):
+  predictions = search(qry, passages = passages, doc_embedding = doc_embedding, top_n=5, )
+  prediction_text = []
+  for hit in predictions:
+    page_content = passages[hit['corpus_id']]
+    metadata = {"source": hit['corpus_id']}
+    result = Document(page_content=page_content, metadata=metadata)
+    prediction_text.append(result)
+  return prediction_text
+
+#mypassages[0]
+
+#mycorpus_embeddings[0][:5]
+
+# query = "What is the name of the plan described by this summary of benefits?"
+# mydocs = get_text_fmt(query)
+# print(len(mydocs))
+# for d in mydocs:
+#   print(d)
+
+# chain_qa.run(input_documents=mydocs, question=query)
+
+def get_llm_response(message):
+  mydocs = get_text_fmt(message)
+  responses = chain_qa.run(input_documents=mydocs, question=message)
+  return responses
+
+"""# chat example"""
+
+def chat(message, history):
+  history = history or []
+  message = message.lower()
+  
+  response = get_llm_response(message)
+  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('# Hack QA')
+  title='Benefit Chatbot'
+  description='chatbot with search on Health Benefits'
+  with gr.Row():
+    chatbot = gr.Chatbot()
+  with gr.Row():
+    message = gr.Textbox(label='Input your question here:',
+                         placeholder='What is the name of the plan described by this summary of benefits?',
+                         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=["What is the name of the plan described by this summary of benefits?",
+                      "How much is the monthly premium?",
+                      "How much do I have to pay if I am admitted to the hospital?"],
+            inputs=message
+        )
+
+demo.launch()
+