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import os | |
from typing import Optional, Tuple | |
import gradio as gr | |
import pickle | |
from query_data import get_chain | |
from threading import Lock | |
with open("vectorstore.pkl", "rb") as f: | |
vectorstore = pickle.load(f) | |
def set_openai_api_key(api_key: str): | |
"""Set the api key and return chain. | |
If no api_key, then None is returned. | |
""" | |
if api_key: | |
os.environ["OPENAI_API_KEY"] = api_key | |
chain = get_chain(vectorstore) | |
os.environ["OPENAI_API_KEY"] = "" | |
return chain | |
class ChatWrapper: | |
def __init__(self): | |
self.lock = Lock() | |
def __call__( | |
self, api_key: str, inp: str, history: Optional[Tuple[str, str]], chain | |
): | |
"""Execute the chat functionality.""" | |
self.lock.acquire() | |
try: | |
history = history or [] | |
# If chain is None, that is because no API key was provided. | |
if chain is None: | |
history.append((inp, "Please paste your OpenAI key to use")) | |
return history, history | |
# Set OpenAI key | |
import openai | |
openai.api_key = api_key | |
# Run chain and append input. | |
output = chain({"question": inp, "chat_history": history})["answer"] | |
history.append((inp, output)) | |
except Exception as e: | |
raise e | |
finally: | |
self.lock.release() | |
return history, history | |
chat = ChatWrapper() | |
block = gr.Blocks(css=".gradio-container {background-color: lightgray}") | |
with block: | |
with gr.Row(): | |
gr.Markdown("<h3><center>Chat-Your-Data (H2 Economics)</center></h3>") | |
openai_api_key_textbox = gr.Textbox( | |
placeholder="Paste your OpenAI API key (sk-...)", | |
show_label=False, | |
lines=1, | |
type="password", | |
) | |
chatbot = gr.Chatbot() | |
with gr.Row(): | |
message = gr.Textbox( | |
label="What's your question?", | |
placeholder="Ask questions about anything covered in the H2 Economics syllabus", | |
lines=1, | |
) | |
submit = gr.Button(value="Send", variant="secondary").style(full_width=False) | |
gr.Examples( | |
examples=[ | |
"Explain real wealth effect.", | |
"Use the real wealth effect to explain the negative gradient of the AD curve.", | |
"Explain the multiplier process.", | |
], | |
inputs=message, | |
) | |
gr.HTML("Demo application of a LangChain chain, built on H2 Economics Data. Many thanks to Jean Chua for giving her notes for this project.") | |
gr.HTML( | |
"<center>Powered by <a href='https://github.com/hwchase17/langchain'>LangChain 🦜️🔗</a></center>" | |
) | |
state = gr.State() | |
agent_state = gr.State() | |
submit.click(chat, inputs=[openai_api_key_textbox, message, state, agent_state], outputs=[chatbot, state]) | |
message.submit(chat, inputs=[openai_api_key_textbox, message, state, agent_state], outputs=[chatbot, state]) | |
openai_api_key_textbox.change( | |
set_openai_api_key, | |
inputs=[openai_api_key_textbox], | |
outputs=[agent_state], | |
) | |
block.launch(debug=True) |