app.py

import os
import gradio as gr
import copy
from llama_cpp import Llama
from huggingface_hub import hf_hub_download
from trimesh.exchange.gltf import export_glb
import trimesh
import numpy as np
import tempfile

# Initialize Llama model from Hugging Face
model = Llama(
    model_path=hf_hub_download(
        repo_id=os.environ.get("REPO_ID", "bartowski/LLaMA-Mesh-GGUF"),
        filename=os.environ.get("MODEL_FILE", "LLaMA-Mesh-Q4_K_L.gguf"),
    )
)

DESCRIPTION = '''
<div>
<h1 style="text-align: center;">LLaMA-Mesh</h1>
<p>LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models.</p>
<p>Supports up to 4096 tokens. Run locally for 8k token context.</p>
<p>To generate another mesh, click "clear" and start a new dialog.</p>
</div>
'''

LICENSE = """
<p/>--- Built with Meta Llama 3.1 8B ---
"""

PLACEHOLDER = """
<div style="padding: 30px; text-align: center;">
   <h1 style="font-size: 28px; opacity: 0.55;">LLaMA-Mesh</h1>
   <p style="font-size: 18px; opacity: 0.65;">Create 3D meshes by chatting.</p>
</div>
"""

css = """
h1 {
  text-align: center;
}

#duplicate-button {
  margin: auto;
  color: white;
  background: #1565c0;
  border-radius: 100vh;
}
"""

def generate_text(message, history, max_tokens=2048, temperature=0.9, top_p=0.95):
    temp = ""
    response = model.create_chat_completion(
        messages=[{"role": "user", "content": message}],
        temperature=temperature,
        max_tokens=max_tokens,
        top_p=top_p,
        stream=True,
    )
    for streamed in response:
        delta = streamed["choices"][0].get("delta", {})
        #print(delta)
        text_chunk = delta.get("content", "")
        
        temp += text_chunk
        yield temp

def apply_gradient_color(mesh_text):
    temp_file = tempfile.NamedTemporaryFile(suffix=".obj", delete=False).name
    with open(temp_file, "w") as f:
        f.write(mesh_text)
    mesh = trimesh.load_mesh(temp_file, file_type='obj')

    vertices = mesh.vertices
    y_values = vertices[:, 1]
    y_normalized = (y_values - y_values.min()) / (y_values.max() - y_values.min())
    colors = np.zeros((len(vertices), 4))
    colors[:, 0] = y_normalized
    colors[:, 2] = 1 - y_normalized
    colors[:, 3] = 1.0
    mesh.visual.vertex_colors = colors

    glb_path = temp_file.replace(".obj", ".glb")
    with open(glb_path, "wb") as f:
        f.write(export_glb(mesh))
    return glb_path

with gr.Blocks(css=css) as demo:
    gr.Markdown(DESCRIPTION)
    chatbot = gr.ChatInterface(
        generate_text,
        title="LLaMA-Mesh | GGUF Integration",
        description="Supports generating 3D meshes with LLaMA-GGUF.",
        examples=[
            ['Create a 3D model of a wooden hammer'],
            ['Create a 3D model of a pyramid in OBJ format'],
            ['Create a 3D model of a table.'],
        ],
        cache_examples=False,
        additional_inputs=[
            gr.Slider(minimum=4096, maximum=16384, value=8192, step=1, label="Max new tokens"),
            gr.Slider(minimum=0.1, maximum=1.5, value=0.9, step=0.1, label="Temperature"),
            gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
        ],
    )

    gr.Markdown("### 3D Mesh Visualization")
    mesh_input = gr.Textbox(
        label="3D Mesh Input",
        placeholder="Paste your 3D mesh in OBJ format here...",
        lines=5,
    )
    visualize_button = gr.Button("Visualize 3D Mesh")
    output_model = gr.Model3D(label="3D Mesh Visualization")
    visualize_button.click(
        fn=apply_gradient_color,
        inputs=[mesh_input],
        outputs=[output_model]
    )
    gr.Markdown(LICENSE)

# Launch the demo
if __name__ == "__main__":
    demo.launch()