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| import gradio as gr | |
| import openai | |
| # Initialize the OpenAI client with your proxy API | |
| client = openai.OpenAI( | |
| api_key= openai_api_key, | |
| base_url="https://aigptx.top/" | |
| ) | |
| # Function to handle predictions | |
| def predict(inputs, top_p, temperature, openai_api_key, system_prompt, chat_counter, chatbot=[], history=[]): | |
| # Build the system prompt if provided | |
| messages = [] | |
| if system_prompt: | |
| messages.append({"role": "system", "content": system_prompt}) | |
| # Add previous conversation history | |
| if chat_counter != 0: | |
| for data in chatbot: | |
| messages.append({"role": "user", "content": data[0]}) | |
| messages.append({"role": "assistant", "content": data[1]}) | |
| # Add the current user input to the messages | |
| messages.append({"role": "user", "content": inputs}) | |
| payload = { | |
| "model": "gpt-3.5-turbo", | |
| "messages": messages, | |
| "temperature": temperature, | |
| "top_p": top_p, | |
| "n": 1, | |
| "stream": True, | |
| "presence_penalty": 0, | |
| "frequency_penalty": 0, | |
| } | |
| # Set the chat counter | |
| chat_counter += 1 | |
| history.append(inputs) | |
| # Using the proxy API to get the response | |
| response = client.Completions.create( | |
| model=payload["model"], | |
| messages=payload["messages"], | |
| temperature=payload["temperature"], | |
| top_p=payload["top_p"], | |
| stream=payload["stream"], | |
| presence_penalty=payload["presence_penalty"], | |
| frequency_penalty=payload["frequency_penalty"] | |
| ) | |
| token_counter = 0 | |
| partial_words = "" | |
| for chunk in response: | |
| if 'choices' in chunk: | |
| delta = chunk['choices'][0]['delta'] | |
| if 'content' in delta: | |
| partial_words += 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)] | |
| token_counter += 1 | |
| yield chat, history, chat_counter | |
| # Function to reset the textbox | |
| def reset_textbox(): | |
| return gr.update(value='') | |
| # UI Components | |
| title = """<h1 align="center">Customizable Chatbot with OpenAI API</h1>""" | |
| description = """ | |
| Explore the outputs of a GPT-3.5 model, with the ability to customize system prompts, enter your OpenAI API key, and interact with a history of conversation logs. | |
| """ | |
| with gr.Blocks(css="""#col_container {width: 1000px; margin-left: auto; margin-right: auto;} | |
| #chatbot {height: 520px; overflow: auto;}""") as demo: | |
| gr.HTML(title) | |
| with gr.Column(elem_id="col_container"): | |
| openai_api_key = gr.Textbox(type='password', label="Enter your OpenAI API key here") | |
| system_prompt = gr.Textbox(placeholder="Enter system prompt (optional)", label="System Prompt", lines=2) | |
| chatbot = gr.Chatbot(elem_id='chatbot') | |
| inputs = gr.Textbox(placeholder="Type your message here!", label="Input", lines=1) | |
| send_btn = gr.Button("Send") | |
| state = gr.State([]) | |
| chat_counter = gr.Number(value=0, visible=False, precision=0) | |
| reset_btn = gr.Button("Reset Chat") | |
| # Input parameters for OpenAI API | |
| with gr.Accordion("Model 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") | |
| # Submit input for model prediction with the send button | |
| send_btn.click(predict, [inputs, top_p, temperature, openai_api_key, system_prompt, chat_counter, chatbot, state], | |
| [chatbot, state, chat_counter]) | |
| reset_btn.click(reset_textbox, [], [inputs]) | |
| inputs.submit(reset_textbox, [], [inputs]) | |
| demo.queue().launch(debug=True) | |