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import gradio as gr
import os
import time
from langchain.document_loaders import OnlinePDFLoader
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 import PromptTemplate
# _template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
# Chat History:
# {chat_history}
# Follow Up Input: {question}
# Standalone question:"""
# CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
# template = """
# You are given the following extracted parts of a long document and a question. Provide a short structured answer.
# If you don't know the answer, look on the web. Don't try to make up an answer.
# Question: {question}
# =========
# {context}
# =========
# Answer in Markdown:"""
def loading_pdf():
return "Loading..."
def pdf_changes(pdf_doc, open_ai_key):
if openai_key is not None:
os.environ['OPENAI_API_KEY'] = open_ai_key
loader = OnlinePDFLoader(pdf_doc.name)
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()
global qa
qa = ConversationalRetrievalChain.from_llm(
llm=OpenAI(temperature=0.5),
retriever=retriever,
return_source_documents=True)
return "Ready"
else:
return "You forgot OpenAI API key"
def add_text(history, text):
history = history + [(text, None)]
return history, ""
def bot(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>YnP LangChain Test </h1>
<p style="text-align: center;">Please specify OpenAI Key before use</p>
</div>
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.HTML(title)
with gr.Column():
openai_key = gr.Textbox(label="You OpenAI API key", type="password")
pdf_doc = gr.File(label="Load a pdf", file_types=['.pdf'], type="file")
with gr.Row():
langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False)
load_pdf = gr.Button("Load pdf to langchain")
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")
load_pdf.click(loading_pdf, None, langchain_status, queue=False)
load_pdf.click(pdf_changes, inputs=[pdf_doc, openai_key], outputs=[langchain_status], queue=False)
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() |