import gradio as gr from llama_cpp import Llama from huggingface_hub import hf_hub_download import random # Initialize model model_path = hf_hub_download( repo_id="AstroMLab/AstroSage-8B-GGUF", filename="AstroSage-8B-Q8_0.gguf" ) llm = Llama( model_path=model_path, n_ctx=2048, n_threads=4, chat_format="llama-3", seed=42, f16_kv=True, logits_all=False, use_mmap=True, use_gpu=True ) # Placeholder responses for when context is empty GREETING_MESSAGES = [ "Greetings! I am AstroSage, your guide to the cosmos. What would you like to explore today?", "Welcome to our cosmic journey! I am AstroSage. How may I assist you in understanding the universe?", "AstroSage here. Ready to explore the mysteries of space and time. How may I be of assistance?", "The universe awaits! I'm AstroSage. What astronomical wonders shall we discuss?", ] def get_random_greeting(): return random.choice(GREETING_MESSAGES) def respond(message, history, system_message, max_tokens, temperature, top_p): 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}) try: response = llm.create_chat_completion( messages=messages, max_tokens=max_tokens, temperature=temperature, top_p=top_p ) return response["choices"][0]["message"]["content"] except Exception as e: return f"Error: {e}" def clear_context(): return [], get_random_greeting() # Gradio Interface with gr.Blocks() as demo: gr.HTML("