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import gradio as gr
import logging, os, sys, threading, time
from agent_langchain import agent_langchain
from agent_llamaindex import agent_llamaindex
from openai import OpenAI
from trace import trace_wandb
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())
lock = threading.Lock()
AGENT_OFF = "Off"
AGENT_LANGCHAIN = "LangChain"
AGENT_LLAMAINDEX = "LlamaIndex"
config = {
"model": "gpt-4o",
"temperature": 0
}
logging.basicConfig(stream = sys.stdout, level = logging.DEBUG)
logging.getLogger().addHandler(logging.StreamHandler(stream = sys.stdout))
def invoke(openai_api_key, prompt, agent_option):
if not openai_api_key:
raise gr.Error("OpenAI API Key is required.")
if not prompt:
raise gr.Error("Prompt is required.")
if not agent_option:
raise gr.Error("Use Agent is required.")
with lock:
os.environ["OPENAI_API_KEY"] = openai_api_key
completion = ""
result = ""
callback = ""
err_msg = ""
try:
start_time_ms = round(time.time() * 1000)
if (agent_option == AGENT_LANGCHAIN):
completion, callback = agent_langchain(
config,
prompt
)
result = completion["output"]
elif (agent_option == AGENT_LLAMAINDEX):
result = agent_llamaindex(
config,
prompt
)
else:
client = OpenAI()
completion = client.chat.completions.create(
messages = [{"role": "user", "content": prompt}],
model = config["model"],
temperature = config["temperature"]
)
callback = completion.usage
result = completion.choices[0].message.content
except Exception as e:
err_msg = e
raise gr.Error(e)
finally:
end_time_ms = round(time.time() * 1000)
trace_wandb(
config,
agent_option,
prompt,
completion,
result,
callback,
err_msg,
start_time_ms,
end_time_ms
)
del os.environ["OPENAI_API_KEY"]
return result
gr.close_all()
demo = gr.Interface(
fn = invoke,
inputs = [gr.Textbox(label = "OpenAI API Key", type = "password", lines = 1),
gr.Textbox(label = "Prompt", lines = 1,
value = "How does current weather in San Francisco and Paris compare in metric and imperial system? Answer in JSON format and include today's date."),
gr.Radio([AGENT_OFF, AGENT_LANGCHAIN, AGENT_LLAMAINDEX], label = "Use Agent", value = AGENT_LANGCHAIN)],
outputs = [gr.Markdown(label = "Completion", value=os.environ["OUTPUT"])],
title = "Agentic Reasoning Application",
description = os.environ["DESCRIPTION"]
)
demo.launch() |