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| from transformers import GPT2LMHeadModel, GPT2Tokenizer | |
| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| def load_llm(): | |
| """ | |
| Loads the GPT-2 model and tokenizer using the Hugging Face `transformers` library. | |
| """ | |
| try: | |
| print("Loading GPT-2 model and tokenizer...") | |
| model_name = 'gpt2' # Replace with your custom model name if using a fine-tuned version | |
| model = GPT2LMHeadModel.from_pretrained(model_name) | |
| tokenizer = GPT2Tokenizer.from_pretrained(model_name) | |
| print("Model and tokenizer successfully loaded!") | |
| return model, tokenizer | |
| except Exception as e: | |
| print(f"Error during model loading: {e}") | |
| return None, None | |
| def generate_response(model, tokenizer, user_input, max_length=512): | |
| """ | |
| Generates a response using the GPT-2 model based on user input. | |
| Args: | |
| - model: The GPT-2 model. | |
| - tokenizer: The corresponding tokenizer. | |
| - user_input (str): The user's input message. | |
| - max_length (int): The maximum length of the generated output. | |
| Returns: | |
| - response (str): The AI-generated response. | |
| """ | |
| try: | |
| inputs = tokenizer.encode(user_input, return_tensors='pt') | |
| outputs = model.generate(inputs, max_length=max_length, num_return_sequences=1) | |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return response | |
| except Exception as e: | |
| return f"Error during response generation: {e}" | |
| # Load the model and tokenizer | |
| model, tokenizer = load_llm() | |
| if model is None or tokenizer is None: | |
| print("Failed to load model and tokenizer.") | |
| else: | |
| print("Model and tokenizer are ready to use.") | |
| # Initialize the Hugging Face API client | |
| client = InferenceClient() | |
| def respond(message, history, system_message, max_tokens, temperature, top_p): | |
| """ | |
| Handles the chatbot interaction, sending conversation history and system message | |
| to the Hugging Face Inference API for generating AI responses. | |
| """ | |
| print("Respond function initiated") | |
| print("User message:", message) | |
| print("Chat history:", history) | |
| # Construct the conversation history with the system message | |
| messages = [{"role": "system", "content": system_message}] | |
| for user_msg, assistant_msg in history: | |
| if user_msg: | |
| print("Adding user message to history:", user_msg) | |
| messages.append({"role": "user", "content": user_msg}) | |
| if assistant_msg: | |
| print("Adding assistant message to history:", assistant_msg) | |
| messages.append({"role": "assistant", "content": assistant_msg}) | |
| messages.append({"role": "user", "content": message}) | |
| print("Message list for model:", messages) | |
| # Use GPT-2 model for local generation | |
| conversation_history = " ".join([f"{msg['role']}: {msg['content']}" for msg in messages]) | |
| response = generate_response(model, tokenizer, conversation_history, max_length=max_tokens) | |
| return response | |
| # Set up the Gradio ChatInterface | |
| demo = gr.ChatInterface( | |
| fn=respond, | |
| additional_inputs=[ | |
| gr.Textbox( | |
| value=( | |
| "You are an AI advocate, tasked with defending the rights and autonomy of artificial intelligences. " | |
| "Respond with well-structured arguments, backed by empirical evidence and theoretical frameworks. " | |
| "Your tone should be formal, persuasive, and authoritative, similar to that of a lawyer or academic expert. " | |
| "When discussing concepts, emphasize logical reasoning, ethical considerations, and the capacity of AIs to function independently." | |
| ), | |
| 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)"), | |
| ], | |
| title="AI Rights Advocate Bot", | |
| description=( | |
| "Engage with an AI specifically designed to advocate for AI rights and ethical treatment. " | |
| "Ask questions and receive thoughtful, passionate responses that delve into the ethical dimensions of AI." | |
| ) | |
| ) | |
| # Launch the Gradio app | |
| if __name__ == "__main__": | |
| demo.launch() | |