import gradio as gr
import torch
from transformers import AutoTokenizer, LlamaForCausalLM
import spaces
import subprocess
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)


# Initialize model and tokenizer
model_id = 'akjindal53244/Llama-3.1-Storm-8B'
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = LlamaForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float32,
    device_map="auto",
    low_cpu_mem_usage=True
)

# Function to format the prompt
def format_prompt(messages):
    prompt = "<|begin_of_text|>"
    for message in messages:
        prompt += f"<|start_header_id|>{message['role']}<|end_header_id|>\n\n{message['content']}<|eot_id|>"
    prompt += "<|start_header_id|>assistant<|end_header_id|>\n\n"
    return prompt

# Function to generate response
@spaces.GPU(duration=300)  # Increased duration due to potential slower processing
def generate_response(message, history):
    messages = [{"role": "system", "content": "You are a helpful assistant."}]
    for human, assistant in history:
        messages.append({"role": "user", "content": human})
        messages.append({"role": "assistant", "content": assistant})
    messages.append({"role": "user", "content": message})

    prompt = format_prompt(messages)
    input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
    generated_ids = model.generate(input_ids, max_new_tokens=256, temperature=0.7, do_sample=True, eos_token_id=tokenizer.eos_token_id)
    response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True)
    return response.strip()

# Create Gradio interface
iface = gr.ChatInterface(
    generate_response,
    title="Llama-3.1-Storm-8B Chatbot",
    description="Chat with the Llama-3.1-Storm-8B model. Type your message and press Enter to send.",
)

# Launch the app
iface.launch()