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| import gradio as gr | |
| import os | |
| import sys | |
| import json | |
| import requests | |
| #Streaming endpoint | |
| API_URL = os.getenv("API_URL") | |
| DISABLED = os.getenv("DISABLED") == 'True' | |
| #Testing with my Open AI Key | |
| OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") | |
| #Supress errors | |
| def exception_handler(exception_type, exception, traceback): | |
| print("%s: %s" % (exception_type.__name__, exception)) | |
| sys.excepthook = exception_handler | |
| sys.tracebacklimit = 0 | |
| 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) | |
| response_code = f"{response}" | |
| if response_code.strip() != "<Response [200]>": | |
| #print(f"response code - {response}") | |
| raise Exception(f"Sorry, hitting rate limit. Please try again later. {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} | |
| print(json.dumps({"chat_counter": chat_counter, "payload": payload, "partial_words": partial_words, "token_counter": token_counter, "counter": counter})) | |
| def reset_textbox(): | |
| return gr.update(value='') | |
| title = """<h1 align="center">🔥GPT4 with ChatCompletions API +🚀Gradio-Streaming</h1>""" | |
| if DISABLED: | |
| title = """<h1 align="center" style="color:red">This app has reached OpenAI's usage limit. We are currently requesting an increase in our quota. Please check back in a few days.</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(visible=not DISABLED).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) | |
| with gr.Modal("User Consent for Data Collection and Use", open=True): | |
| gr.HTML("""<p> | |
| By using our app powered by OpenAI's API, you acknowledge and agree that we may collect and use the data you provide, including the inputs you type into our app and the outputs generated by OpenAI's API. Your data may be published or shared with others. | |
| </p> | |
| <p> | |
| If you do not agree with our data collection and use practices, please do not use our app. By continuing to use our app, you are providing your explicit consent to the collection, use, and potential sharing of your data as described above. | |
| </p>""") | |
| accept_button = gr.Button("I Agree", id="accept_consent") | |
| # Disable chatbot, inputs, and buttons until consent is given | |
| chatbot.set_disabled(True) | |
| inputs.set_disabled(True) | |
| b1.set_disabled(True) | |
| def enable_inputs(): | |
| chatbot.set_disabled(False) | |
| inputs.set_disabled(False) | |
| b1.set_disabled(False) | |
| accept_button.on_click(enable_inputs) | |
| 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() | |