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| import os | |
| import gradio as gr | |
| from openai import OpenAI | |
| from typing import List, Tuple | |
| # Define available models | |
| AVAILABLE_MODELS = { | |
| "DeepSeek V3 (Hyperbolic.xyz)": "deepseek-ai/DeepSeek-V3", | |
| "DeepSeek V3 (HuggingFace.co)": "deepseek-ai/DeepSeek-V3", | |
| "Llama3.3-70b-Instruct": "meta-llama/Llama-3.3-70B-Instruct", | |
| "Llama3.1-8b-Instruct": "meta-llama/Meta-Llama-3.1-8B-Instruct", | |
| } | |
| HYPERB_ENDPOINT_URL = "https://api.hyperbolic.xyz/v1" | |
| HF_ENDPOINT_URL = "https://huggingface.co/api/inference-proxy/together" | |
| HYPERB_API_KEY = os.getenv('HYPERBOLIC_XYZ_KEY') | |
| HF_API_KEY = os.getenv('HF_KEY') | |
| PASSWORD = os.getenv("PASSWD") # Store the password in an environment variable | |
| hyperb_client = OpenAI(base_url=HYPERB_ENDPOINT_URL, api_key=HYPERB_API_KEY) | |
| hf_client = OpenAI(base_url=HF_ENDPOINT_URL, api_key=HF_API_KEY) | |
| def respond( | |
| message: str, | |
| history: List[Tuple[str, str]], | |
| system_message: str, | |
| model_choice: str, | |
| max_tokens: int, | |
| temperature: float, | |
| top_p: float, | |
| ): | |
| 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}) | |
| response = "" | |
| if "(HuggingFace.co)" in model_choice: | |
| this_client = hf_client | |
| else: | |
| this_client = hyperb_client | |
| for chunk in this_client.chat.completions.create( | |
| model=AVAILABLE_MODELS[model_choice], # Use the selected model | |
| messages=messages, | |
| max_tokens=max_tokens, | |
| temperature=temperature, | |
| top_p=top_p, | |
| stream=True, | |
| ): | |
| token = chunk.choices[0].delta.content or "" | |
| response += token | |
| yield response | |
| def check_password(input_password): | |
| if input_password == PASSWORD: | |
| return gr.update(visible=False), gr.update(visible=True) | |
| else: | |
| return gr.update(value="", interactive=True), gr.update(visible=False) | |
| with gr.Blocks() as demo: | |
| with gr.Column(): | |
| password_input = gr.Textbox( | |
| type="password", label="Enter Password", interactive=True | |
| ) | |
| submit_button = gr.Button("Submit") | |
| error_message = gr.Textbox( | |
| label="Error", visible=False, interactive=False | |
| ) | |
| with gr.Column(visible=False) as chat_interface: | |
| chat = gr.ChatInterface( | |
| respond, | |
| api_name=False, | |
| additional_inputs=[ | |
| gr.Textbox(value="You are a helpful assistant.", label="System message"), | |
| gr.Dropdown( | |
| choices=list(AVAILABLE_MODELS.keys()), | |
| value=list(AVAILABLE_MODELS.keys())[0], | |
| label="Select Model" | |
| ), | |
| gr.Slider(minimum=1, maximum=30000, value=2048, step=100, 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)"), | |
| ], | |
| ) | |
| submit_button.click(check_password, inputs=password_input, outputs=[password_input, chat_interface]) | |
| if __name__ == "__main__": | |
| demo.launch(share=True) |