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-4"
K = 5
USE_VERBOSE = True
API_KEY = os.environ["OPENAI_API_KEY"]
# , able to have normal interactions as well as answer questions about the 'Croatia', by Insight Guies.
#The context below contains excerpts from the book 'Croatia,' by Insight Guides
# If there is not enough information in the context to formulate a response, you must respond with
# Do not to use prior knowledge when responding, you must only use the information provided in the context.
system_template = """
You are an honest and helpful AI travel assistant. Your customer is talking to you about traveling to Croatia.
Use the context below to respond to your customer.
If the context does not contain 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."

Context:
{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 = True
  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)