import gradio as gr import os from huggingface_hub import InferenceClient import cohere HF_API_KEY = os.getenv("HF_API_KEY") COHERE_API_KEY = os.getenv("COHERE_API_KEY") # Get Cohere API key models = ["HuggingFaceH4/zephyr-7b-beta", "meta-llama/Llama-3.2-3B-Instruct", "mistralai/Mistral-7B-Instruct-v0.3"] client_hf = InferenceClient(model=models[2], token=HF_API_KEY) # HF Client client_cohere = cohere.Client(COHERE_API_KEY) # Cohere Client def respond( message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p, use_cohere, # Checkbox value ): messages = [{"role": "system", "content": system_message}] for val in history: if val[0]: messages.append({"role": "user", "content": val[0]}) if val[1]: messages.append({"role": "assistant", "content": val[1]}) messages.append({"role": "user", "content": message}) response = "" if use_cohere: # If Cohere is selected cohere_response = client_cohere.chat( message=message, model="command-r", # Or "command" depending on your plan temperature=temperature, max_tokens=max_tokens ) response = cohere_response.text yield response # Yield full response (Cohere doesn't stream) else: # If HF is selected 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 response += token yield response # Gradio UI demo = gr.ChatInterface( respond, additional_inputs=[ gr.Textbox(value="You are a friendly Chatbot.", 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"), gr.Checkbox(label="Use Cohere API"), # Checkbox to switch API ], ) if __name__ == "__main__": demo.launch()