app.py

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

# HTML template for custom UI
HTML_TEMPLATE = """
<style>
    .llama-image {
        display: flex;
        justify-content: center;
        margin-bottom: 20px;
    }
    .llama-image img {
        max-width: 300px;
        border-radius: 10px;
        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
    }
    .llama-description {
        text-align: center;
        font-weight: bold;
        margin-top: 10px;
    }
</style>
<div class="llama-image">
    <img src="https://cdn-uploads.huggingface.co/production/uploads/64c75c1237333ccfef30a602/tmOlbERGKP7JSODa6T06J.jpeg" alt="Llama">
    <div class="llama-description">Llama-3.1-Storm-8B Model</div>
</div>
<h1>Llama-3.1-Storm-8B Text Generation</h1>
<p>Generate text using the powerful Llama-3.1-Storm-8B model. Enter a prompt and let the AI create!</p>
"""

# Load the model and tokenizer
model_name = "akjindal53244/Llama-3.1-Storm-8B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

@spaces.GPU(duration=120)
def generate_text(prompt, max_length, temperature):
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": prompt}
    ]
    formatted_prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
    
    inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device)
    
    outputs = model.generate(
        **inputs,
        max_new_tokens=max_length,
        do_sample=True,
        temperature=temperature,
        top_k=100,
        top_p=0.95,
    )
    
    return tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)

# Create Gradio interface
iface = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.Textbox(lines=5, label="Prompt"),
        gr.Slider(minimum=1, maximum=500, value=128, step=1, label="Max Length"),
        gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"),
    ],
    outputs=gr.Textbox(lines=10, label="Generated Text"),
    title="Llama-3.1-Storm-8B Text Generation",
    description="Enter a prompt to generate text using the Llama-3.1-Storm-8B model.",
    article=None,
    css=".gradio-container {max-width: 800px; margin: auto;}",
)

iface.launch(
    additional_inputs=[
        gr.HTML(HTML_TEMPLATE)
    ]
)