Spaces:
Running
Running
File size: 2,895 Bytes
aa0eed8 12338d7 92fb7b8 031211a aa0eed8 53ed856 ee66ad7 29455b5 53ed856 12c2b66 92fb7b8 12c2b66 68cb77a 53ed856 5295650 29455b5 53ed856 68cb77a 53ed856 aaf4e3a 881c209 53ed856 453ee12 53ed856 9ea93d2 453ee12 d06678c 881c209 453ee12 cee6a57 12c2b66 24755bb 38dfd80 24755bb 3017744 cee6a57 d42fd6b df144ba 7fd13ba 53ed856 29455b5 53ed856 29455b5 53ed856 cee6a57 881c209 53ed856 a030fa6 cee6a57 24755bb 12c2b66 805ff2f 19797f3 8186c74 431abc0 c4a7aa8 bd63387 19797f3 8fae4d3 19797f3 1ba7dce bd63387 d42fd6b |
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 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 |
import gradio as gr
import json, openai, os, time
from openai import OpenAI
_client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
_assistant, _thread = None, None
def create_assistant(client):
assistant = client.beta.assistants.create(
name="Math Tutor",
instructions="You are a personal math tutor. Answer questions briefly, in a sentence or less.",
model="gpt-4-1106-preview",
)
show_json("assistant", assistant)
return assistant
def create_thread(client):
thread = client.beta.threads.create()
show_json("thread", thread)
return thread
def create_message(client, thread, msg):
message = client.beta.threads.messages.create(
role="user",
thread_id=thread.id,
content=msg,
)
show_json("message", message)
return message
def create_run(client, assistant, thread):
run = client.beta.threads.runs.create(
assistant_id=assistant.id,
thread_id=thread.id,
)
show_json("run", run)
return run
def wait_on_run(client, thread, run):
while run.status == "queued" or run.status == "in_progress":
run = client.beta.threads.runs.retrieve(
thread_id=thread.id,
run_id=run.id,
)
time.sleep(0.25)
show_json("run", run)
return run
def list_messages(client, thread):
messages = client.beta.threads.messages.list(
thread_id=thread.id
)
show_json("messages", messages)
return messages
def extract_content_values(data):
content_values = []
for item in data.data:
for content in item.content:
if content.type == 'text':
content_values.append(content.text.value)
return content_values
def show_json(str, obj):
print(f"=> {str}\n{json.loads(obj.model_dump_json())}")
def chat(message, history, openai_api_key):
global _client, _assistant, _thread
if _assistant == None:
_assistant = create_assistant(_client)
if _thread == None:
_thread = create_thread(_client)
create_message(_client, _thread, message)
# async
run = create_run(_client, _assistant, _thread)
run = wait_on_run(_client, _thread, run)
messages = list_messages(client, thread)
return extract_content_values(messages)[0]
gr.ChatInterface(
chat,
chatbot=gr.Chatbot(height=300),
textbox=gr.Textbox(placeholder="Question", container=False, scale=7),
title="Multi-Assistant Demo",
description="Ask AAA Assistant, BBB Assistant, and CCC Assistant any question",
retry_btn=None,
undo_btn=None,
clear_btn="Clear",
examples=[["I need to solve the equation '2x + 10 = 7.5'. Can you help me?", "sk-<BringYourOwn>"]],
cache_examples=False,
additional_inputs=[
gr.Textbox("sk-", label="OpenAI API Key", type = "password"),
],
).launch() |