app.py

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

model_name = "mistralai/Mistral-7B-v0.1"
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).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(text_input):
    texts = text_input.split('\n')
    embeddings = [get_embedding(text) for text in texts]
    embeddings_3d = [reduce_to_3d(emb) for emb in embeddings]
    
    fig = go.Figure()

    # Add origin point (black)
    fig.add_trace(go.Scatter3d(x=[0], y=[0], z=[0], mode='markers', name='Origin',
                               marker=dict(size=5, color='black')))

    # Add lines and points for each text embedding
    colors = ['red', 'blue', 'green', 'purple', 'orange', 'cyan', 'magenta', 'yellow']
    for i, emb in enumerate(embeddings_3d):
        color = colors[i % len(colors)]
        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}',
                                   line=dict(color=color), marker=dict(color=color)))

    fig.update_layout(scene=dict(xaxis_title='X', yaxis_title='Y', zaxis_title='Z'))
    
    return fig

iface = gr.Interface(
    fn=compare_embeddings,
    inputs=[
        gr.Textbox(label="Input Texts", lines=5, placeholder="Enter multiple texts, each on a new line")
    ],
    outputs=gr.Plot(),
    title="3D Embedding Comparison",
    description="Compare the embeddings of multiple strings visualized in 3D space using Mistral 7B."
)

iface.launch()