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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| # Hugging Face client initialization | |
| client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| # Function to handle NLP responses and interaction with the model | |
| def respond(message, history, system_message, max_tokens, temperature, top_p): | |
| """ | |
| Function to handle user message and generate a response using the NLP model. | |
| Parameters: | |
| message (str): User's current message/input. | |
| history (list): List of tuples representing conversation history (user's and assistant's messages). | |
| system_message (str): System-level instructions to the assistant to guide its responses. | |
| max_tokens (int): Maximum number of tokens to generate in the response. | |
| temperature (float): Degree of randomness in the response generation. | |
| top_p (float): Controls the diversity of the response using nucleus sampling. | |
| Yields: | |
| str: Streamed response as tokens are generated. | |
| """ | |
| # Prepare the message for the assistant, including system-level instructions and history. | |
| messages = [{"role": "system", "content": system_message}] | |
| # Loop through the history and add past conversation to the messages | |
| for user_message, assistant_message in history: | |
| if user_message: | |
| messages.append({"role": "user", "content": user_message}) | |
| if assistant_message: | |
| messages.append({"role": "assistant", "content": assistant_message}) | |
| # Append the current user message to the conversation | |
| messages.append({"role": "user", "content": message}) | |
| # Initialize the response variable | |
| response = "" | |
| # Get the response stream from the Hugging Face model | |
| for message in client.chat_completion( | |
| messages, | |
| max_tokens=max_tokens, | |
| stream=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ): | |
| # Extract the token content and append it to the response | |
| token = message.choices[0].delta.content | |
| response += token | |
| yield response | |
| # System prompt to guide the assistant's behavior | |
| default_system_message = ( | |
| "You are NLPToolkit Agent, an advanced natural language processing assistant. " | |
| "You specialize in tasks such as text summarization, sentiment analysis, text classification, " | |
| "entity recognition, and answering technical questions about NLP models and datasets. " | |
| "Assist users with clear, concise, and actionable outputs." | |
| ) | |
| # Create the Gradio interface for user interaction | |
| def create_interface(): | |
| """ | |
| Create and return a Gradio interface for the NLPToolkit Agent with customizable parameters. | |
| Parameters: | |
| None | |
| Returns: | |
| gr.Interface: The Gradio interface object. | |
| """ | |
| return gr.ChatInterface( | |
| respond, | |
| additional_inputs=[ | |
| gr.Textbox( | |
| value=default_system_message, | |
| label="System Message" | |
| ), | |
| gr.Slider( | |
| minimum=1, | |
| maximum=2048, | |
| 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)" | |
| ), | |
| ], | |
| ) | |
| # Run the Gradio interface | |
| if __name__ == "__main__": | |
| demo = create_interface() | |
| demo.launch() |