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| import gradio as gr | |
| import os | |
| from huggingface_hub import InferenceClient | |
| import cohere | |
| # Models, API keys and initialization of API clients | |
| COHERE_MODEL = "command-r-plus" | |
| HF_MODEL = "meta-llama/Llama-3.2-3B-Instruct" | |
| HF_API_KEY = os.getenv("HF_API_KEY") | |
| COHERE_API_KEY = os.getenv("COHERE_API_KEY") | |
| client_hf = InferenceClient(model=HF_MODEL, token=HF_API_KEY) | |
| client_cohere = cohere.Client(COHERE_API_KEY) | |
| def respond( | |
| message: str, | |
| history: list[tuple[str, str]], | |
| system_message: str, | |
| max_tokens: int, | |
| temperature: float, | |
| top_p: float, | |
| use_cohere: bool | |
| ): | |
| """Handles chatbot responses based on user input and chat history. | |
| This function integrates with either the Cohere API or Hugging Face API to generate AI-based responses. | |
| Args: | |
| message (str): The latest user message. | |
| history (list[tuple[str, str]]): A list of previous exchanges where: | |
| - Each tuple contains (user_message, assistant_response). | |
| - Example: [("Hello", "Hi there!"), ("How are you?", "I'm good!")] | |
| system_message (str): A system-level instruction for the chatbot (e.g., personality, style). | |
| max_tokens (int): Maximum number of new tokens the model can generate. | |
| temperature (float): Controls randomness (higher = more varied responses). | |
| top_p (float): Probability threshold for token selection (higher = more diverse responses). | |
| use_cohere (bool): If True, uses Cohere API; otherwise, uses Hugging Face API. | |
| Yields: | |
| str: The chatbot's response (streamed for Hugging Face, full response for Cohere). | |
| """ | |
| # Constructing the message history for context | |
| messages = [{"role": "system", "content": system_message}] | |
| for user_msg, assistant_msg in history: | |
| if user_msg: | |
| messages.append({"role": "user", "content": user_msg}) | |
| if assistant_msg: | |
| messages.append({"role": "assistant", "content": assistant_msg}) | |
| messages.append({"role": "user", "content": message}) # Append current user message | |
| response = "" | |
| if use_cohere: | |
| # Using Cohere API (no streaming support) | |
| cohere_response = client_cohere.chat( | |
| message=message, | |
| model=COHERE_MODEL, | |
| temperature=temperature, | |
| max_tokens=max_tokens | |
| ) | |
| response = cohere_response.text | |
| yield response # Yield full response immediately | |
| else: | |
| # Using Hugging Face API (streaming responses) | |
| for message in client_hf.chat_completion( | |
| messages, | |
| max_tokens=max_tokens, | |
| stream=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ): | |
| token = message.choices[0].delta.content # Extract generated token | |
| response += token | |
| yield response # Yield response incrementally | |
| # Gradio UI with user-configurable inputs | |
| demo = gr.ChatInterface( | |
| respond, | |
| additional_inputs=[ | |
| gr.Textbox(value="You are a friendly Chatbot.", label="System prompt"), # System instruction | |
| gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), # Token limit | |
| gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), # Randomness control | |
| gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"), # Probability mass | |
| gr.Checkbox(label="Use capable Cohere model instead."), # API selection toggle | |
| ], | |
| ) | |
| # Start Gradio interface | |
| if __name__ == "__main__": | |
| demo.launch() | |