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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, 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=[] | |
| 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(): | |
| if counter == 0: | |
| counter+=1 | |
| continue | |
| counter+=1 | |
| # check whether each line is non-empty | |
| if chunk : | |
| # decode each line as response data is in bytes | |
| if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0: | |
| break | |
| #print(json.loads(chunk.decode()[6:])['choices'][0]["delta"]["content"]) | |
| partial_words = partial_words + json.loads(chunk.decode()[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 🚀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-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('''<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="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) | |