import spaces import gradio as gr from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer import torch import random # Define model parameters for 8-bit quantized loading model_name = "AstroMLab/AstroSage-8B" # Load the tokenizer tokenizer = AutoTokenizer.from_pretrained(model_name) # Load the model with 8-bit quantization using bitsandbytes model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.float16, load_in_8bit=True, # Enable 8-bit quantization device_map="auto" # Automatically assign layers to available GPUs ) streamer = TextStreamer(tokenizer) # 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 user(user_message, history): """Add user message to chat history.""" if history is None: history = [] return "", history + [{"role": "user", "content": user_message}] @spaces.GPU(duration=20) def bot(history): """Generate the chatbot response.""" if not history: history = [] # Prepare input prompt for the model system_prompt = ( "You are AstroSage, an intelligent AI assistant specializing in astronomy, astrophysics, and cosmology. " "Provide accurate, scientific information while making complex concepts accessible. " "You're enthusiastic about space exploration and maintain a sense of wonder about the cosmos." ) # Construct the chat history as a single input string prompt = system_prompt + "\n\n" for message in history: if message["role"] == "user": prompt += f"User: {message['content']}\n" else: prompt += f"AstroSage: {message['content']}\n" prompt += "AstroSage: " # Generate response inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate( **inputs, max_new_tokens=512, temperature=0.7, top_p=0.95, do_sample=True, streamer=streamer ) # Decode the generated output and update history response_text = tokenizer.decode(outputs[0], skip_special_tokens=True) response_text = response_text[len(prompt):].strip() history.append({"role": "assistant", "content": response_text}) yield history def initial_greeting(): """Return properly formatted initial greeting.""" return [{"role": "assistant", "content": random.choice(GREETING_MESSAGES)}] # Custom CSS for a space theme custom_css = """ #component-0 { background-color: #1a1a2e; border-radius: 15px; padding: 20px; } .dark { background-color: #0f0f1a; } .contain { max-width: 1200px !important; } """ # Create the Gradio interface with gr.Blocks(css=custom_css, theme=gr.themes.Soft(primary_hue="indigo", neutral_hue="slate")) as demo: gr.Markdown( """ # 🌌 AstroSage: Your Cosmic AI Companion Welcome to AstroSage, an advanced AI assistant specializing in astronomy, astrophysics, and cosmology. Powered by the AstroSage-Llama-3.1-8B model, I'm here to help you explore the wonders of the universe! ### What Can I Help You With? - 🪐 Explanations of astronomical phenomena - 🚀 Space exploration and missions - ⭐ Stars, galaxies, and cosmology - 🌍 Planetary science and exoplanets - 📊 Astrophysics concepts and theories - 🔭 Astronomical instruments and observations Just type your question below and let's embark on a cosmic journey together! """ ) chatbot = gr.Chatbot( label="Chat with AstroSage", bubble_full_width=False, show_label=True, height=450, type="messages" ) with gr.Row(): msg = gr.Textbox( label="Type your message here", placeholder="Ask me anything about space and astronomy...", scale=9 ) clear = gr.Button("Clear Chat", scale=1) # Example questions for quick start gr.Examples( examples=[ "What is a black hole and how does it form?", "Can you explain the life cycle of a star?", "What are exoplanets and how do we detect them?", "Tell me about the James Webb Space Telescope.", "What is dark matter and why is it important?" ], inputs=msg, label="Example Questions" ) # Set up the message chain with streaming msg.submit( user, [msg, chatbot], [msg, chatbot], queue=False ).then( bot, chatbot, chatbot ) # Clear button functionality clear.click(lambda: None, None, chatbot, queue=False) # Initial greeting demo.load(initial_greeting, None, chatbot, queue=False) # Launch the app if __name__ == "__main__": demo.launch()