Spaces:
Sleeping
Sleeping
File size: 2,110 Bytes
3369d9f 192dc63 3369d9f cdc9be2 192dc63 3369d9f 192dc63 3369d9f 192dc63 cdc9be2 3369d9f 4fa6af3 3369d9f 4fa6af3 3369d9f 192dc63 3369d9f 4fa6af3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
# Application file for Gradio App for OpenAI Model
import gradio as gr
import time
from lc_base.chain import openai_chain
import os
from lc_base.logs import save_log
dir = os.path.join("outputs", "combined", "policy_eu_asia_usa", "faiss_index")
# dir = os.path.join("outputs", "policy", "1", "faiss_index")
title = """<h1 align="center">Chat</h1>"""
description = """<br><br><h3 align="center">This is a literature chat model, which can currently answer questions to AI Policies provided.</h3>"""
def save_api_key(api_key):
os.environ['OPENAI_API_KEY'] = str(api_key)
return f"API Key saved in the environment: {api_key}"
def user(user_message, history):
return "", history + [[user_message, None]]
def respond(message, chat_history):
question = str(message)
chain = openai_chain(inp_dir=dir)
start_time = time.time()
output = chain.get_response(query=question, k=100, model_name="gpt-4-1106-preview", type="stuff")
print(output)
time_taken = time.time() - start_time
save_log(file_path='logs/policy_combined.csv', query=question, response=output, model_name="gpt-4-1106-preview", time_taken=time_taken, inp="Policy", data="Policy/1")
bot_message = output
chat_history.append((message, bot_message))
time.sleep(2)
return " ", chat_history
with gr.Blocks(theme=gr.themes.Soft(primary_hue="emerald", neutral_hue="slate")) as chat:
gr.HTML(title)
api_key_input = gr.Textbox(lines=1, label="Enter your OpenAI API Key")
api_key_input_submit = api_key_input.submit(save_api_key, [api_key_input])
chatbot = gr.Chatbot(height=750)
msg = gr.Textbox(label="Send a message", placeholder="Send a message",
show_label=False, container=False)
msg.submit(respond, [msg, chatbot], [msg, chatbot])
gr.Examples([
["What are the challenges and opportunities of AI in supply chain management?"],
["What does these documents talk about?"],
], inputs=msg, label= "Click on any example to copy in the chatbox"
)
gr.HTML(description)
chat.queue()
chat.launch()
|