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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 'How to Win Friends & Influence People,' by Dail Carnegie. 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 How to Win Friends & Influence People."
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/How_To_Win_Friends_And_Influence_People_-_Dale_Carnegie.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>How to Win Friends & Influence People</h1>")
with gr.Row():
gr.Markdown("by Dale Carnegie")
chatbot = gr.Chatbot(elem_id="chatbot").style(height=600)
with gr.Row():
message = gr.Textbox(
label="",
placeholder="How to Win Friends...",
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="How do I know if I'm talking about myself too much?", variant="primary")
ex1.click(getanswer, inputs=[chain_state, ex1, state], outputs=[chatbot, state, message])
ex2 = gr.Button(value="What do people enjoy talking about the most?", variant="primary")
ex2.click(getanswer, inputs=[chain_state, ex2, state], outputs=[chatbot, state, message])
ex4 = gr.Button(value="Why should I try to get along with people better?", variant="primary")
ex4.click(getanswer, inputs=[chain_state, ex4, state], outputs=[chatbot, state, message])
ex5 = gr.Button(value="How do I cite a Reddit thread?", variant="primary")
ex5.click(getanswer, inputs=[chain_state, ex5, state], outputs=[chatbot, state, message])
predictBtn = gr.Button(value="Predict", visible=False)
predictBtn.click(predict, inputs=[message], outputs=[message])
block.launch(debug=True) |