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| import gradio as gr | |
| # from huggingface_hub import InferenceClient | |
| from transformers import pipeline | |
| import os | |
| # Retrieve the Hugging Face API token from environment variables | |
| hf_token = os.getenv("HF_TOKEN") | |
| if not api_token: | |
| raise ValueError("API token is not set. Please set the HF_TOKEN environment variable in Space Settings.") | |
| """ | |
| For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference | |
| """ | |
| # requires space hardware update to use large models (TODO) | |
| # client = InferenceClient("mistralai/Mistral-Large-Instruct-2407") | |
| # Note change in instantiation*** | |
| text_generator = pipeline("text-generation", model="google/gemma-2-2b", use_auth_token=hf_token) | |
| def authenticate_and_generate(token, message, history, system_message, max_tokens, temperature, top_p): | |
| # Initialize the text-generation pipeline with the provided token | |
| text_generator = pipeline("text-generation", model="google/gemma-2-2b", use_auth_token=token) | |
| # Construct the prompt with system message, history, and user input | |
| prompt = system_message + "\n" + "\n".join([f"User: {msg[0]}\nAssistant: {msg[1]}" for msg in history if msg[0] and msg[1]]) | |
| prompt += f"\nUser: {message}\nAssistant:" | |
| # Generate a response using the model | |
| response = text_generator(prompt, max_length=max_tokens, temperature=temperature, top_p=top_p, do_sample=True, truncation=True) | |
| # Extract the generated text from the response list | |
| assistant_response = response[0]['generated_text'] | |
| # Optionally trim the assistant response if it includes the prompt again | |
| assistant_response = assistant_response.split("Assistant:", 1)[-1].strip() | |
| return assistant_response | |
| """ | |
| For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface | |
| """ | |
| athena = gr.ChatInterface( | |
| fn=authenticate_and_generate, | |
| additional_inputs=[ | |
| gr.Textbox( | |
| label="Hugging Face API Token", | |
| type="password", | |
| placeholder="Please provide a Hugging Face auth token.", | |
| lines=1, | |
| max_lines=1 | |
| ), | |
| gr.Textbox(value= | |
| """ | |
| You are a marketing-minded content writer for Plan.com (a UK telecommunications company). | |
| You will be provided a bullet-point list of guidelines from which to generate an article to be published in the company News section of the website. | |
| Please follow these guidelines: | |
| - Always speak using British English expressions, syntax, and spelling. | |
| - Make the articles engaging and fun, but also professional and informative. | |
| To provide relevant contextual information about the company, please source information from the following websites: | |
| - https://plan.com/our-story | |
| - https://plan.com/products-services | |
| - https://plan.com/features/productivity-and-performance | |
| - https://plan.com/features/security-and-connectivity | |
| - https://plan.com/features/connectivity-and-cost | |
| """, | |
| label="System message"), | |
| gr.Slider(minimum=1, maximum=4096, value=512, step=1, label="Max new tokens"), | |
| gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| gr.Slider( | |
| minimum=0.1, | |
| maximum=1.0, | |
| value=0.95, | |
| step=0.05, | |
| label="Top-p (nucleus sampling)", | |
| ), | |
| ], | |
| ) | |
| if __name__ == "__main__": | |
| athena.launch() |