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import gradio as gr
import os
import time
from langchain.document_loaders import UnstructuredMarkdownLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.llms import OpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain
from langchain.prompts.prompt import PromptTemplate
os.environ['OPENAI_API_KEY'] = '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'
_template = """Assume you are He Yingxu, please complete the following conversations:
Chat History:
{chat_history}
Follow Up Input: {question}
"""
CUSTOM_QUESTION_PROMPT = PromptTemplate.from_template(_template)
loader = UnstructuredMarkdownLoader('docs/resume.md')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = Chroma.from_documents(texts, embeddings)
retriever = db.as_retriever()
qa = ConversationalRetrievalChain.from_llm(
llm=OpenAI(temperature=0.3),
retriever=retriever,
condense_question_prompt=CUSTOM_QUESTION_PROMPT,
return_source_documents=False)
def add_text(history, text):
history = history + [(text, None)]
return history, ""
def bot(history):
print(history)
response = infer(history[-1][0], history)
history[-1][1] = ""
for character in response:
history[-1][1] += character
time.sleep(0.05)
yield history
def infer(question, history):
res = []
for human, ai in history[:-1]:
pair = (human, ai)
res.append(pair)
chat_history = res
#print(chat_history)
query = question
result = qa({"question": query, "chat_history": chat_history})
#print(result)
return result["answer"]
css = """
#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
"""
title = """
<div style="text-align: center;max-width: 700px;">
<h1>Chat with PDF • OpenAI</h1>
<p style="text-align: center;">Upload a .PDF from your computer, click the "Load PDF to LangChain" button, <br />
when everything is ready, you can start asking questions about the pdf ;) <br />
This version is set to store chat history, and uses OpenAI as LLM, don't forget to copy/paste your OpenAI API key</p>
</div>
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.HTML(title)
chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350)
question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
submit_btn = gr.Button("Send Message")
question.submit(add_text, [chatbot, question], [chatbot, question]).then(
bot, chatbot, chatbot
)
submit_btn.click(add_text, [chatbot, question], [chatbot, question]).then(
bot, chatbot, chatbot)
demo.launch()
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