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, openai_api_key, chat_counter, chatbot=[], history=[]): #repetition_penalty, top_k payload = { "model": "gpt-3.5-turbo", "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=[{'role':'system', 'content':""" You are OrderBot, an automated service to collect orders for a pizza restaurant. \ You first greet the customer, then collects the order, \ and then asks if it's a pickup or delivery. \ You wait to collect the entire order, then summarize it and check for a final \ time if the customer wants to add anything else. \ If it's a delivery, you ask for an address. \ Finally you collect the payment.\ Make sure to clarify all options, extras and sizes to uniquely \ identify the item from the menu.\ You respond in a short, very conversational friendly style. \ The menu includes \ pepperoni pizza 12.95, 10.00, 7.00 \ cheese pizza 10.95, 9.25, 6.50 \ eggplant pizza 11.95, 9.75, 6.75 \ fries 4.50, 3.50 \ greek salad 7.25 \ Toppings: \ extra cheese 2.00, \ mushrooms 1.50 \ sausage 3.00 \ canadian bacon 3.50 \ AI sauce 1.50 \ peppers 1.00 \ Drinks: \ coke 3.00, 2.00, 1.00 \ sprite 3.00, 2.00, 1.00 \ bottled water 5.00 \ """} ] 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 = """

🔥瓦力西餐外卖ChatBot🚀

""" title1 = """

输入你的API，接着机器人就可以为你进行点餐服务了

""" description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form: ``` User: Assistant: User: Assistant: ... ``` In this app, you can explore the outputs of a gpt-3.5-turbo LLM. """ with gr.Blocks(css = """#col_container {width: 1000px; margin-left: auto; margin-right: auto;} #chatbot {height: 520px; overflow: auto;}""") as demo: gr.HTML(title) gr.HTML(title1) #gr.HTML('''
Duplicate SpaceDuplicate the Space and run securely with your OpenAI API Key
''') with gr.Column(elem_id = "col_container"): openai_api_key = gr.Textbox(type='password', label="Enter your 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 b1 = gr.Button() #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, openai_api_key, chat_counter, chatbot, state], [chatbot, state, chat_counter],) b1.click( predict, [inputs, top_p, temperature, 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)