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# import for typing
from langchain.chains import RetrievalQAWithSourcesChain
# gradio
import gradio as gr
global qa
from qa import qa
#####
#
# Gradio fns
####
def create_gradio_interface(qa:RetrievalQAWithSourcesChain):
"""
Create a gradio interface for the QA model
"""
def add_text(history, text):
history = history + [(text, None)]
return history, ""
def bot(history):
response = infer(history[-1][0], history)
sources = [doc.metadata.get("source") for doc in response['source_documents']]
src_list = '\n'.join(sources)
print_this = response['answer'] + "\n\n\n Sources: \n\n\n" + src_list
history[-1][1] = print_this #response['answer']
return history
def infer(question, history):
query = question
result = qa({"query": query, "history": history, "question": question})
return result
css="""
#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
"""
title = """
<div style="text-align: center;max-width: 1920px;">
<h1>Chat with your Documentation</h1>
<p style="text-align: center;">This is a privately hosten Docs AI Buddy, <br />
It will help you with any question regarding the documentation of Ray ;)</p>
</div>
"""
with gr.Blocks(css=css) as demo:
with gr.Column(min_width=900, elem_id="col-container"):
gr.HTML(title)
chatbot = gr.Chatbot([], elem_id="chatbot")
#with gr.Row():
# clear = gr.Button("Clear")
with gr.Row():
question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
with gr.Row():
clear = gr.ClearButton([chatbot, question])
question.submit(add_text, [chatbot, question], [chatbot, question], queue=False).then(
bot, chatbot, chatbot
)
#clear.click(lambda: None, None, chatbot, queue=False)
return demo
if __name__ == "__main__":
demo = create_gradio_interface(qa)
demo.queue().launch()
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