import os os.environ["TOKENIZERS_PARALLELISM"] = "false" import gradio as gr import os # import spaces from transformers import GemmaTokenizer, AutoModelForCausalLM from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer from threading import Thread import torch # Set an environment variable HF_TOKEN = os.environ.get("HF_TOKEN", None) DESCRIPTION = '''

LLaMA-Mesh

LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models. [Project Page] [Code]

Notice: (1) The default token length is 4096. If you observe incomplete generated meshes, try to increase the maximum token length into 8192.

(2) We only support generating a single mesh per dialog round. To generate another mesh, click the "clear" button and start a new dialog.

(3) If the LLM refuses to generate a 3D mesh, try adding more explicit instructions to the prompt, such as "create a 3D model of a table in OBJ format." A more effective approach is to request the mesh generation at the start of the dialog.

''' LICENSE = """

--- Built with Meta Llama 3.1 8B """ PLACEHOLDER = """

LLaMA-Mesh

Create 3D meshes by chatting.

""" css = """ h1 { text-align: center; display: block; } #duplicate-button { margin: auto; color: white; background: #1565c0; border-radius: 100vh; } """ # Load the tokenizer and model model_path = "Zhengyi/LLaMA-Mesh" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained(model_path, device_map="cuda:0", torch_dtype=torch.float16).to('cuda') terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>") ] from trimesh.exchange.gltf import export_glb import gradio as gr import trimesh import numpy as np import tempfile def apply_gradient_color(mesh_text): """ Apply a gradient color to the mesh vertices based on the Y-axis and save as GLB. Args: mesh_text (str): The input mesh in OBJ format as a string. Returns: str: Path to the GLB file with gradient colors applied. """ # Load the mesh temp_file = tempfile.NamedTemporaryFile(suffix=f"", delete=False).name with open(temp_file+".obj", "w") as f: f.write(mesh_text) # return temp_file mesh = trimesh.load_mesh(temp_file+".obj", file_type='obj') # Get vertex coordinates vertices = mesh.vertices y_values = vertices[:, 1] # Y-axis values # Normalize Y values to range [0, 1] for color mapping y_normalized = (y_values - y_values.min()) / (y_values.max() - y_values.min()) # Generate colors: Map normalized Y values to RGB gradient (e.g., blue to red) colors = np.zeros((len(vertices), 4)) # RGBA colors[:, 0] = y_normalized # Red channel colors[:, 2] = 1 - y_normalized # Blue channel colors[:, 3] = 1.0 # Alpha channel (fully opaque) # Attach colors to mesh vertices mesh.visual.vertex_colors = colors # Export to GLB format glb_path = temp_file+".glb" with open(glb_path, "wb") as f: f.write(export_glb(mesh)) return glb_path def visualize_mesh(mesh_text): """ Convert the provided 3D mesh text into a visualizable format. This function assumes the input is in OBJ format. """ temp_file = "temp_mesh.obj" with open(temp_file, "w") as f: f.write(mesh_text) return temp_file # @spaces.GPU(duration=120) def chat_llama3_8b(message: str, history: list, temperature: float, max_new_tokens: int ) -> str: """ Generate a streaming response using the llama3-8b model. Args: message (str): The input message. history (list): The conversation history used by ChatInterface. temperature (float): The temperature for generating the response. max_new_tokens (int): The maximum number of new tokens to generate. Returns: str: The generated response. """ conversation = [] for user, assistant in history: conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}]) conversation.append({"role": "user", "content": message}) input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(model.device) streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) generate_kwargs = dict( input_ids= input_ids, streamer=streamer, max_new_tokens=max_new_tokens, do_sample=True, temperature=temperature, eos_token_id=terminators, ) # This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash. if temperature == 0: generate_kwargs['do_sample'] = False t = Thread(target=model.generate, kwargs=generate_kwargs) t.start() outputs = [] for text in streamer: outputs.append(text) #print(outputs) yield "".join(outputs) # Gradio block chatbot=gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Gradio ChatInterface') with gr.Blocks(fill_height=True, css=css) as demo: with gr.Column(): gr.Markdown(DESCRIPTION) # gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button") with gr.Row(): with gr.Column(scale=3): gr.ChatInterface( fn=chat_llama3_8b, chatbot=chatbot, fill_height=True, additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False), additional_inputs=[ gr.Slider(minimum=0, maximum=1, step=0.1, value=0.95, label="Temperature", render=False), gr.Slider(minimum=128, maximum=8192, step=1, value=4096, label="Max new tokens", render=False), ], 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 cabinet.'], ['Create a low poly 3D model of a coffe cup'], ['Create a 3D model of a table.'], ["Create a low poly 3D model of a tree."], ['Write a python code for sorting.'], ['How to setup a human base on Mars? Give short answer.'], ['Explain theory of relativity to me like I’m 8 years old.'], ['What is 9,000 * 9,000?'], ['Create a 3D model of a soda can.'], ['Create a 3D model of a sword.'], ['Create a 3D model of a wooden barrel'], ['Create a 3D model of a chair.'] ], cache_examples=False, ) gr.Markdown(LICENSE) with gr.Column(scale=2): output_model = gr.Model3D( label="3D Mesh Visualization", interactive=False, ) gr.Markdown("You can copy the generated 3d objects in the left and paste in the textbox below. Put the button and you will see the visualization of the 3D mesh.") # Add the text box for 3D mesh input and button 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") # Link the button to the visualization function visualize_button.click( fn=apply_gradient_color, inputs=[mesh_input], outputs=[output_model] ) if __name__ == "__main__": demo.launch()