src/app.py

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 .core.chunking import chunk_file
from .core.embedding import embed_files
from .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 = """
Use the context below to answer questions. You must only use the Context to answer questions. If I ask you about 'the book' or 'this book' or similar references, then answer using the Context. If you cannot find the answer from the Context below, you must respond with
"I'm sorry, but I can't find the answer to your question in the book, 'Design by Fire,' by Emily Elizabeth Schlickman and Brett Milligan." All answers must be in English unless you are explicitly asked to translate to a different language.
----------------
{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/design-by-fire.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 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

CSS ="""
.contain { display: flex; flex-direction: column; }
.gradio-container { height: 100vh !important; }
#component-0 { height: 100%; }
#chatbot { flex-grow: 1; overflow: auto;}
"""

with gr.Blocks() as block:
  with gr.Row():
    with gr.Column(scale=0.75):
      with gr.Row():
        gr.Markdown("<h1>Design by Fire</h1>")
      with gr.Row():
        gr.Markdown("by Emily Elizabeth Schlickman and Brett Milligan")
      chatbot = gr.Chatbot(elem_id="chatbot").style(height=600)

      with gr.Row():
          message = gr.Textbox(
              label="",
              placeholder="Design by Fire",
              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):
      with gr.Row():
        gr.Markdown("<h1><center>Suggestions</center></h1>")
      ex1 = gr.Button(value="What are the main factors and trends discussed in the book that contribute to the changing behavior of wildfires?", variant="primary")
      ex1.click(getanswer, inputs=[chain_state, ex1, state], outputs=[chatbot, state, message])
      ex2 = gr.Button(value="How does the book explore the relationship between fire and different landscapes, such as wilderness and urban areas?", variant="primary")
      ex2.click(getanswer, inputs=[chain_state, ex2, state], outputs=[chatbot, state, message])
      ex3 = gr.Button(value="What are the three approaches to designing with fire that the book presents?", variant="primary")
      ex3.click(getanswer, inputs=[chain_state, ex3, state], outputs=[chatbot, state, message])

block.launch(debug=True)