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"] #print("===") #print(result) #print("===") 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()