app.py

import gradio as gr
import spaces
import torch
from transformers import AutoTokenizer, AutoModel
import plotly.graph_objects as go

# Update the model name to Llama 3.1
model_name = "meta-llama/Meta-Llama-3.1-405B-Instruct-FP8"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = None

# Set pad token to eos token if not defined
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

@spaces.GPU
def get_embedding(text):
    global model
    if model is None:
        model = AutoModel.from_pretrained(model_name, torch_dtype=torch.float16).cuda()
        model.resize_token_embeddings(len(tokenizer))
    
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512).to('cuda')
    with torch.no_grad():
        outputs = model(**inputs)
    return outputs.last_hidden_state.mean(dim=1).squeeze().cpu().numpy()

def reduce_to_3d(embedding):
    return embedding[:3]

@spaces.GPU
def compare_embeddings(*texts):
    embeddings = [get_embedding(text) for text in texts if text.strip()]
    embeddings_3d = [reduce_to_3d(emb) for emb in embeddings]
    
    fig = go.Figure()
    
    for i, emb in enumerate(embeddings_3d):
        fig.add_trace(go.Scatter3d(x=[0, emb[0]], y=[0, emb[1]], z=[0, emb[2]], 
                                   mode='lines+markers', name=f'Text {i+1}'))
    
    fig.update_layout(scene=dict(xaxis_title='X', yaxis_title='Y', zaxis_title='Z'))
    
    return fig

def generate_text_boxes(n):
    return [gr.Textbox(label=f"Text {i+1}", visible=(i < n)) for i in range(10)]

with gr.Blocks() as iface:
    gr.Markdown("# 3D Embedding Comparison")
    gr.Markdown("Compare the embeddings of multiple strings visualized in 3D space using Llama 3.1.")
    
    num_texts = gr.Slider(minimum=2, maximum=10, step=1, value=2, label="Number of texts to compare")
    
    with gr.Column() as input_column:
        text_boxes = generate_text_boxes(2)
    
    output = gr.Plot()
    
    compare_button = gr.Button("Compare Embeddings")
    
    def update_interface(n):
        return [gr.update(visible=(i < n)) for i in range(10)]

    num_texts.change(
        update_interface,
        inputs=[num_texts],
        outputs=text_boxes
    )
    
    compare_button.click(
        compare_embeddings,
        inputs=text_boxes,
        outputs=output
    )

iface.launch()