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# TODO: Gradio session / multi-user thread | |
# Reference: | |
# | |
# https://vimeo.com/990334325/56b552bc7a | |
# https://platform.openai.com/playground/assistants | |
# https://cookbook.openai.com/examples/assistants_api_overview_python | |
# https://platform.openai.com/docs/api-reference/assistants/createAssistant | |
# https://platform.openai.com/docs/assistants/tools | |
import gradio as gr | |
import json#, openai, os, time | |
from assistants import ( | |
openai_client, | |
assistant, | |
thread, | |
create_assistant, | |
load_assistant, | |
create_thread, | |
create_message, | |
create_run, | |
wait_on_run, | |
get_run_steps, | |
execute_tool_calls, | |
get_messages, | |
extract_content_values, | |
) | |
def chat(message, history): | |
if not message: | |
raise gr.Error("Message is required.") | |
global assistant, thread | |
if assistant == None: | |
#assistant = create_assistant(openai_client) # on first run, create assistant and update assistant_id | |
# see https://platform.openai.com/playground/assistants | |
assistant = load_assistant(openai_client) # on subsequent runs, load assistant | |
if thread == None or len(history) == 0: | |
thread = create_thread(openai_client) | |
create_message(openai_client, thread, message) | |
run = create_run(openai_client, assistant, thread) | |
run = wait_on_run(openai_client, thread, run) | |
run_steps = get_run_steps(openai_client, thread, run) | |
### TODO | |
tool_call_ids, tool_call_results = execute_tool_calls(run_steps) | |
if len(tool_call_ids) > 0: | |
# https://platform.openai.com/docs/api-reference/runs/submitToolOutputs | |
tool_output = {} | |
try: | |
tool_output = { | |
"tool_call_id": tool_call_ids[0], | |
"output": tool_call_results[0].to_json() | |
} | |
except AttributeError: | |
tool_output = { | |
"tool_call_id": tool_call_ids[0], | |
"output": tool_call_results[0] | |
} | |
run = openai_client.beta.threads.runs.submit_tool_outputs( | |
thread_id=thread.id, | |
run_id=run.id, | |
tool_outputs=[tool_output] | |
) | |
run = wait_on_run(openai_client, thread, run) | |
run_steps = get_run_steps(openai_client, thread, run) | |
### | |
tool_call_ids, tool_call_results = execute_tool_calls(run_steps) | |
if len(tool_call_ids) > 1: | |
# https://platform.openai.com/docs/api-reference/runs/submitToolOutputs | |
tool_output = {} | |
try: | |
tool_output = { | |
"tool_call_id": tool_call_ids[1], | |
"output": tool_call_results[1].to_json() | |
} | |
except AttributeError: | |
tool_output = { | |
"tool_call_id": tool_call_ids[1], | |
"output": tool_call_results[1] | |
} | |
run = openai_client.beta.threads.runs.submit_tool_outputs( | |
thread_id=thread.id, | |
run_id=run.id, | |
tool_outputs=[tool_output] | |
) | |
run = wait_on_run(openai_client, thread, run) | |
run_steps = get_run_steps(openai_client, thread, run) | |
### | |
messages = get_messages(openai_client, thread) | |
text_values, image_values = extract_content_values(messages) | |
download_link = "" | |
if len(image_values) > 0: | |
download_link = f"<p>Download: https://platform.openai.com/storage/files/{image_values[0]}</p>" | |
return f"{'<hr>'.join(list(reversed(text_values))[1:])}{download_link}" | |
gr.ChatInterface( | |
fn=chat, | |
chatbot=gr.Chatbot(height=350), | |
textbox=gr.Textbox(placeholder="Ask anything", container=False, scale=7), | |
title="Python Coding Assistant", | |
description=( | |
"The assistant can **generate, explain, fix, optimize,** and **document Python code, " | |
"create unit test cases,** and **answer general coding-related questions.** " | |
"It can also **execute code**. " | |
"The assistant has access to a <b>today tool</b> (get current date), to a " | |
"**yfinance download tool** (get stock data), and to a " | |
"**tavily search tool** (web search)." | |
), | |
clear_btn="Clear", | |
retry_btn=None, | |
undo_btn=None, | |
examples=[ | |
["Generate: Python code to fine-tune model meta-llama/Meta-Llama-3.1-8B on dataset gretelai/synthetic_text_to_sql using QLoRA"], | |
["Explain: r\"^(?=.*[A-Z])(?=.*[a-z])(?=.*[0-9])(?=.*[\\W]).{8,}$\""], | |
["Fix: x = [5, 2, 1, 3, 4]; print(x.sort())"], | |
["Optimize: x = []; for i in range(0, 10000): x.append(i)"], | |
["Execute: First 25 Fibbonaci numbers"], | |
["Execute with tools: Create a plot showing stock gain QTD for NVDA and AMD, x-axis is \"Day\" and y-axis is \"Gain %\""], | |
["Execute with tools: Get key announcements from the latest OpenAI Dev Day"] | |
], | |
cache_examples=False, | |
).launch() |