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| import gradio as gr | |
| from openai import OpenAI | |
| def complete_text(prompt, max_tokens, temperature, top_p, openai_api_key): | |
| """ | |
| Get a plain text completion from OpenAI gpt-3.5-turbo-instruct. | |
| """ | |
| if not openai_api_key: | |
| return "⚠️ Please enter a valid OpenAI API key." | |
| client = OpenAI(api_key=openai_api_key) | |
| response_text = "" | |
| stream = client.completions.create( | |
| model="gpt-3.5-turbo-instruct", | |
| prompt=prompt, | |
| max_tokens=max_tokens, | |
| temperature=temperature, | |
| top_p=top_p, | |
| stream=True, | |
| ) | |
| for event in stream: | |
| if hasattr(event, "choices") and event.choices: | |
| token = event.choices[0].text or "" | |
| response_text += token | |
| yield response_text | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## ✍️ Text Completion Demo (OpenAI gpt-3.5-turbo-instruct)") | |
| gr.Markdown("Enter a prompt, adjust decoding parameters, and watch the model complete your text.") | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| prompt = gr.Textbox( | |
| label="Prompt", | |
| placeholder="Type the beginning of your text...", | |
| lines=4, | |
| ) | |
| max_tokens = gr.Slider( | |
| minimum=1, maximum=1024, value=100, step=1, label="Max tokens" | |
| ) | |
| temperature = gr.Slider( | |
| minimum=0.0, maximum=2.0, value=0.7, step=0.1, label="Temperature" | |
| ) | |
| top_p = gr.Slider( | |
| minimum=0.1, maximum=1.0, value=1.0, step=0.05, label="Top-p" | |
| ) | |
| api_key = gr.Textbox( | |
| placeholder="sk-... Paste your OpenAI API key here", | |
| label="🔑 OpenAI API Key", | |
| type="password", | |
| ) | |
| submit = gr.Button("Generate Completion") | |
| with gr.Column(scale=3): | |
| output = gr.Textbox( | |
| label="Generated Completion", | |
| lines=15, | |
| ) | |
| submit.click( | |
| fn=complete_text, | |
| inputs=[prompt, max_tokens, temperature, top_p, api_key], | |
| outputs=output, | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |