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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, openai_api_key, chat_counter, chatbot=[], history=[]): #repetition_penalty, top_k
temperature = 1.0
top_p = 1.0
payload = {
"model": "gpt-3.5-turbo",
"messages": [{"role": "user", "content": f"{inputs}"}],
"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-3.5-turbo",
"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)
#response = requests.post(API_URL, headers=headers, json=payload, stream=True)
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 # resembles {chatbot: chat, state: history}
def reset_textbox():
return gr.update(value='')
title = """<h1 align="center">ChatGPT API</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-3.5-turbo LLM.
"""
with gr.Blocks(css = """#col_container {margin-left: auto; margin-right: auto;}
#chatbot {height: 520px; overflow: auto;}""") as demo:
gr.HTML(title)
#gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/ChatGPTwithAPI?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"):
openai_api_key = gr.Textbox(type='password', label="请输入您的 OpenAI API 密钥")
chatbot = gr.Chatbot(elem_id='chatbot') #c
inputs = gr.Textbox(placeholder= "你好！", label= "请输入您的问题并按Run发送") #t
state = gr.State([]) #s
b1 = gr.Button()
#inputs, top_p, temperature, top_k, repetition_penalty
with gr.Accordion("参数", 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, openai_api_key, chat_counter, chatbot, state], [chatbot, state, chat_counter],)
b1.click( predict, [inputs, openai_api_key, chat_counter, chatbot, state], [chatbot, state, chat_counter],)
b1.click(reset_textbox, [], [inputs])
inputs.submit(reset_textbox, [], [inputs])
#gr.Markdown(description)
demo.queue().launch(debug=True) |