ChatGPT-4

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ChatGPT-4 / app.py

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Duplicate from ysharma/ChatGPT4

a5c4c65 about 1 year ago

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	import gradio as gr
	import os
	import json
	import requests

	#Streaming endpoint
	API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"

	#Testing with my Open AI Key
	OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

	def predict(inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):

	payload = {
	"model": "gpt-4",
	"messages": [{"role": "user", "content": f"{inputs}"}],
	"temperature" : 1.0,
	"top_p":1.0,
	"n" : 1,
	"stream": True,
	"presence_penalty":0,
	"frequency_penalty":0,
	}

	headers = {
	"Content-Type": "application/json",
	"Authorization": f"Bearer {OPENAI_API_KEY}"
	}

	print(f"chat_counter - {chat_counter}")
	if chat_counter != 0 :
	messages=[]
	for data in chatbot:
	temp1 = {}
	temp1["role"] = "user"
	temp1["content"] = data[0]
	temp2 = {}
	temp2["role"] = "assistant"
	temp2["content"] = data[1]
	messages.append(temp1)
	messages.append(temp2)
	temp3 = {}
	temp3["role"] = "user"
	temp3["content"] = inputs
	messages.append(temp3)
	#messages
	payload = {
	"model": "gpt-4",
	"messages": messages, #[{"role": "user", "content": f"{inputs}"}],
	"temperature" : temperature, #1.0,
	"top_p": top_p, #1.0,
	"n" : 1,
	"stream": True,
	"presence_penalty":0,
	"frequency_penalty":0,
	}

	chat_counter+=1

	history.append(inputs)
	print(f"payload is - {payload}")
	# make a POST request to the API endpoint using the requests.post method, passing in stream=True
	response = requests.post(API_URL, headers=headers, json=payload, stream=True)
	print(f"response code - {response}")
	token_counter = 0
	partial_words = ""

	counter=0
	for chunk in response.iter_lines():
	#Skipping first chunk
	if counter == 0:
	counter+=1
	continue
	#counter+=1
	# check whether each line is non-empty
	if chunk.decode() :
	chunk = chunk.decode()
	# decode each line as response data is in bytes
	if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
	#if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
	# break
	partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
	if token_counter == 0:
	history.append(" " + partial_words)
	else:
	history[-1] = partial_words
	chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ] # convert to tuples of list
	token_counter+=1
	yield chat, history, chat_counter, response # resembles {chatbot: chat, state: history}


	def reset_textbox():
	return gr.update(value='')

	title = """<h1 align="center">🔥GPT4 with ChatCompletions API +🚀Gradio-Streaming</h1>"""
	description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form:
	```
	User: <utterance>
	Assistant: <utterance>
	User: <utterance>
	Assistant: <utterance>
	...
	```
	In this app, you can explore the outputs of a gpt-4 LLM.
	"""

	theme = gr.themes.Default(primary_hue="green")

	with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;}
	#chatbot {height: 520px; overflow: auto;}""",
	theme=theme) as demo:
	gr.HTML(title)
	gr.HTML("""<h3 align="center">🔥This Huggingface Gradio Demo provides you full access to GPT4 API (4096 token limit). 🎉🥳🎉You don't need any OPENAI API key🙌</h1>""")
	gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/ChatGPT4?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>''')
	with gr.Column(elem_id = "col_container"):
	#GPT4 API Key is provided by Huggingface
	#openai_api_key = gr.Textbox(type='password', label="Enter only your GPT4 OpenAI API key here")
	chatbot = gr.Chatbot(elem_id='chatbot') #c
	inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter") #t
	state = gr.State([]) #s
	with gr.Row():
	with gr.Column(scale=7):
	b1 = gr.Button().style(full_width=True)
	with gr.Column(scale=3):
	server_status_code = gr.Textbox(label="Status code from OpenAI server", )

	#inputs, top_p, temperature, top_k, repetition_penalty
	with gr.Accordion("Parameters", open=False):
	top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
	temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
	#top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k",)
	#repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", )
	chat_counter = gr.Number(value=0, visible=False, precision=0)

	inputs.submit( predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],) #openai_api_key
	b1.click( predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],) #openai_api_key
	b1.click(reset_textbox, [], [inputs])
	inputs.submit(reset_textbox, [], [inputs])

	#gr.Markdown(description)
	demo.queue(max_size=20, concurrency_count=10).launch(debug=True)