src/app.py

import json
import os
from threading import Lock
from typing import Any, Dict, Optional, Tuple

import gradio as gr
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.prompts.chat import (ChatPromptTemplate,
                                    HumanMessagePromptTemplate,
                                    SystemMessagePromptTemplate)

from src.core.chunking import chunk_file
from src.core.embedding import embed_files
from src.core.parsing import read_file

VECTOR_STORE = "faiss"
MODEL = "openai"
EMBEDDING = "openai"
MODEL = "gpt-3.5-turbo-16k"
K = 5
USE_VERBOSE = True
API_KEY = os.environ["OPENAI_API_KEY"]
system_template = """
The context below contains excerpts from 'Croatia,' by Insight Guides. You must only use the information in the context below to formulate your response. If there is not enough information to formulate  a response, you must respond with
"I'm sorry, but I can't find the answer to your question in, the book Croatia by Insight Guides."

Begin context:
{context}
End context.

{chat_history}
"""

# Create the chat prompt templates
messages = [
  SystemMessagePromptTemplate.from_template(system_template),
  HumanMessagePromptTemplate.from_template("{question}")
]
qa_prompt = ChatPromptTemplate.from_messages(messages)

class AnswerConversationBufferMemory(ConversationBufferMemory):
  def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
    return super(AnswerConversationBufferMemory, self).save_context(inputs,{'response': outputs['answer']})

def getretriever():
  with open("./resources/RG_Croatia_9ed_FINAL-919-745-7.pdf", 'rb') as uploaded_file:
    try:
      file = read_file(uploaded_file)
    except Exception as e:
      print(e)

  chunked_file = chunk_file(file, chunk_size=512, chunk_overlap=0)
  folder_index = embed_files(
    files=[chunked_file],
    embedding=EMBEDDING,
    vector_store=VECTOR_STORE,
    openai_api_key=API_KEY,
  )
  return folder_index.index.as_retriever(verbose=True, search_type="similarity", search_kwargs={"k": K})

retriever = getretriever()

def predict(message):
  print(message)
  msgJson = json.loads(message)
  print(msgJson)
  messages = [
    SystemMessagePromptTemplate.from_template(system_template),
    HumanMessagePromptTemplate.from_template("{question}")
  ]
  qa_prompt = ChatPromptTemplate.from_messages(messages)

  llm = ChatOpenAI(
        openai_api_key=API_KEY,
        model_name=MODEL,
        verbose=True)
  memory = AnswerConversationBufferMemory(memory_key="chat_history", return_messages=True)
  for msg in msgJson["history"]:
    memory.save_context({"input": msg[0]}, {"answer": msg[1]})

  chain = ConversationalRetrievalChain.from_llm(
    llm,
    retriever=retriever,
    return_source_documents=USE_VERBOSE,
    memory=memory,
    verbose=USE_VERBOSE,
    combine_docs_chain_kwargs={"prompt": qa_prompt})
  chain.rephrase_question = False
  lock = Lock()
  lock.acquire()
  try:
    output = chain({"question": msgJson["question"]})
    output = output["answer"]
  except Exception as e:
    print(e)
    raise e
  finally:
    lock.release()
  return output

def getanswer(chain, question, history):
  if hasattr(chain, "value"):
    chain = chain.value
  if hasattr(history, "value"):
    history = history.value
  if hasattr(question, "value"):
    question = question.value

  history = history or []
  lock = Lock()
  lock.acquire()
  try:
    output = chain({"question": question})
    output = output["answer"]
    history.append((question, output))
  except Exception as e:
    raise e
  finally:
    lock.release()
  return history, history, gr.update(value="")

def load_chain(inputs = None):
  llm = ChatOpenAI(
        openai_api_key=API_KEY,
        model_name=MODEL,
        verbose=True)
  chain = ConversationalRetrievalChain.from_llm(
    llm,
    retriever=retriever,
    return_source_documents=USE_VERBOSE,
    memory=AnswerConversationBufferMemory(memory_key="chat_history", return_messages=True),
    verbose=USE_VERBOSE,
    combine_docs_chain_kwargs={"prompt": qa_prompt})
  return chain

with gr.Blocks() as block:
  with gr.Row():
    with gr.Column(scale=0.75):
      with gr.Row():
        gr.Markdown("<h1>Croatia</h1>")
      with gr.Row():
        gr.Markdown("by Insight Guides")
      chatbot = gr.Chatbot(elem_id="chatbot").style(height=600)

      with gr.Row():
          message = gr.Textbox(
              label="",
              placeholder="Ask Croatia...",
              lines=1,
          )
      with gr.Row():
          submit = gr.Button(value="Send", variant="primary", scale=1)

      state = gr.State()
      chain_state = gr.State(load_chain)

      submit.click(getanswer, inputs=[chain_state, message, state], outputs=[chatbot, state, message])
      message.submit(getanswer, inputs=[chain_state, message, state], outputs=[chatbot, state, message])

    with gr.Column(scale=0.25):
      predictBtn = gr.Button(value="Predict", visible=False)
      predictBtn.click(predict, inputs=[message], outputs=[message])

block.launch(debug=True)