app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model and tokenizer with CPU optimizations
model = AutoModelForCausalLM.from_pretrained(
    "hackergeek/gemma-finetuned",
    torch_dtype=torch.float32,  # Changed to float32 for CPU compatibility
    device_map="cpu"            # Force CPU usage
)
tokenizer = AutoTokenizer.from_pretrained("hackergeek/gemma-finetuned")
tokenizer.pad_token = tokenizer.eos_token

# Explicitly move model to CPU (redundant but safe)
model.to("cpu")

def format_prompt(message, history):
    """Format the prompt with conversation history"""
    system_prompt = "You are a knowledgeable space expert assistant. Answer questions about astronomy, space exploration, and related topics in a clear and engaging manner."
    prompt = f"<system>{system_prompt}</system>\n"
    
    for user_msg, bot_msg in history:
        prompt += f"<user>{user_msg}</user>\n<assistant>{bot_msg}</assistant>\n"
    
    prompt += f"<user>{message}</user>\n<assistant>"
    return prompt

def respond(message, history):
    # Format the prompt with conversation history
    full_prompt = format_prompt(message, history)
    
    # Tokenize input (keep on CPU)
    inputs = tokenizer(full_prompt, return_tensors="pt", add_special_tokens=False)
    
    # Generate response with CPU-friendly parameters
    outputs = model.generate(
        input_ids=inputs.input_ids,
        attention_mask=inputs.attention_mask,
        max_new_tokens=512,        # Reduced for faster CPU processing
        temperature=0.7,
        top_p=0.85,
        repetition_penalty=1.1,
        do_sample=True,
        no_repeat_ngram_size=2      # Added to reduce repetition
    )
    
    # Decode response
    response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
    
    return response

# Simplified CSS for better CPU rendering
space_css = """
.gradio-container { background: #000000; color: #ffffff; }
.chatbot { background: #0a0a2a !important; }
"""

with gr.Blocks(css=space_css) as demo:
    gr.Markdown("# 🚀 CPU Space Chatbot 🌌")
    gr.Markdown("Note: Responses may be slower due to CPU processing")
    
    chatbot = gr.ChatInterface(
        respond,
        examples=[
            "What is a neutron star?",
            "Explain the Big Bang theory",
            "How do rockets work?",
            "What's the temperature on Venus?"
        ],
        clear_btn="Clear",
    )
    chatbot.chatbot.height = 500

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)