from PIL import Image, ImageDraw, ImageFont import tempfile import gradio as gr from smolagents import CodeAgent, InferenceClientModel from smolagents import DuckDuckGoSearchTool, Tool from diffusers import DiffusionPipeline import torch from smolagents import OpenAIServerModel import os from huggingface_hub import login openai_key = os.environ.get("OPENAI_API_KEY") hf_token = os.environ.get("HF_TOKEN") if hf_token: login(token=hf_token) else: print("Warning: HF_TOKEN not set.") if openai_key: # Exemplo de como usar a OpenAI API key print("OpenAI API key is set") else: print("Warning: OPENAI_API_KEY not set.") print("HF_TOKEN set?", "Yes" if hf_token else "No") print("OPENAI_API_KEY set?", "Yes" if openai_key else "No") # ========================================================= # Utility functions # ========================================================= def add_label_to_image(image, label): draw = ImageDraw.Draw(image) font_path = "/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf" font_size = 30 try: font = ImageFont.truetype(font_path, font_size) except: font = ImageFont.load_default() text_bbox = draw.textbbox((0, 0), label, font=font) text_width, text_height = text_bbox[2] - text_bbox[0], text_bbox[3] - text_bbox[1] position = (image.width - text_width - 20, image.height - text_height - 20) rect_margin = 10 rect_position = [ position[0] - rect_margin, position[1] - rect_margin, position[0] + text_width + rect_margin, position[1] + text_height + rect_margin, ] draw.rectangle(rect_position, fill=(0, 0, 0, 128)) draw.text(position, label, fill="white", font=font) return image def plot_and_save_agent_image(agent_image, label, save_path=None): #pil_image = agent_image.to_raw() pil_image = agent_image labeled_image = add_label_to_image(pil_image, label) #labeled_image.show() if save_path: labeled_image.save(save_path) print(f"Image saved to {save_path}") else: print("No save path provided. Image not saved.") def generate_prompts_for_object(object_name): return { "past": f"Show an old version of a {object_name} from its early days.", "present": f"Show a {object_name} with current features/design/technology.", "future": f"Show a futuristic version of a {object_name}, by predicting advanced features and futuristic design." } # ========================================================= # Tool and Agent Initialization # ========================================================= image_generation_tool = Tool.from_space( #"KingNish/Realtime-FLUX", "black-forest-labs/FLUX.1-schnell", api_name="/infer", name="image_generator", description="Generate an image from a prompt" ) search_tool = DuckDuckGoSearchTool() #llm_engine = InferenceClientModel("Qwen/Qwen2.5-72B-Instruct") llm_engine2 = InferenceClientModel("Qwen/Qwen2.5-Coder-32B-Instruct", provider="together") # Inicialização do modelo OpenAI com smolagents llm_engine = OpenAIServerModel( model_id="gpt-4o-mini", # Exemplo: ajuste para o modelo OpenAI que deseja usar api_base="https://api.openai.com/v1", api_key=openai_key ) agent = CodeAgent(tools=[image_generation_tool, search_tool], model=llm_engine) # ========================================================= # Main logic for image generation # ========================================================= from PIL import Image def generate_object_history(object_name): images = [] prompts = generate_prompts_for_object(object_name) general_instruction = ( "Search the necessary information and features for the following prompt, " "then generate an image of it." ) image_paths = [] for time_period, prompt in prompts.items(): print(f"Generating {time_period} frame: {prompt}") try: result = agent.run( general_instruction, additional_args={"prompt": prompt, "width": 256, # specify width "height": 256, # specify height "seed": 0, # optional seed "randomize_seed": False, # optional "num_inference_steps": 4 # optional } ) # result is tuple: (filepath, seed) if isinstance(result, (list, tuple)): image_filepath = result[0] else: image_filepath = result # fallback in case result is just a string # Open the image from filepath image = Image.open(image_filepath) # Save the image to your naming convention image_filename = f"{object_name}_{time_period}.png" image.save(image_filename) # Optional: call your plotting function (if needed) plot_and_save_agent_image(image, f"{object_name} - {time_period.title()}", save_path=image_filename) image_paths.append(image_filename) images.append(image) except Exception as e: print(f"Agent failed on {time_period}: {e}") continue # Create GIF from generated images if any gif_path = f"{object_name}_evolution.gif" if images: images[0].save(gif_path, save_all=True, append_images=images[1:], duration=1000, loop=0) return image_paths, gif_path # ========================================================= # Gradio Interface # ========================================================= def create_gradio_interface(): with gr.Blocks() as demo: gr.Markdown("# TimeMetamorphy: An Object Evolution Generator") gr.Markdown(""" Explore how everyday objects evolved over time. Enter an object name like "phone", "car", or "bicycle" and see its past, present, and future visualized with AI! """) default_images = [ "car_past2.png", "car_present2.png", "car_future2.png" ] default_gif_path = "car_evolution2.gif" with gr.Row(): with gr.Column(): object_name_input = gr.Textbox(label="Enter an object name", placeholder="e.g. bicycle, car, phone") generate_button = gr.Button("Generate Evolution") image_gallery = gr.Gallery(label="Generated Images", columns=3, rows=1, value=default_images, type="filepath") gif_output = gr.Image(label="Generated GIF", value=default_gif_path, type="filepath") #image_gallery = gr.Gallery(label="Generated Images", columns=3, rows=1, type="filepath") #gif_output = gr.Image(label="Generated GIF", type="filepath") generate_button.click(fn=generate_object_history, inputs=[object_name_input], outputs=[image_gallery, gif_output]) return demo # Launch the interface demo = create_gradio_interface() demo.launch()