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| from transformers import load_tool, ReactCodeAgent, HfApiEngine | |
| from PIL import Image, ImageDraw, ImageFont | |
| import tempfile | |
| import gradio as gr | |
| #%% Methods | |
| # Function to add a label to an image | |
| def add_label_to_image(image, label): | |
| # Create a drawing context | |
| draw = ImageDraw.Draw(image) | |
| # Define font size and color (adjust font path for your environment) | |
| font_path = "/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf" # Example font path | |
| font_size = 30 # Larger font size for better visibility | |
| try: | |
| font = ImageFont.truetype(font_path, font_size) | |
| except: | |
| font = ImageFont.load_default() | |
| # Calculate the size and position of the text (aligned to the left) | |
| 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)# right-aligned with margin | |
| # Add a semi-transparent rectangle behind the text for better visibility | |
| 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)) # Semi-transparent black | |
| draw.text(position, label, fill="white", font=font) | |
| return image | |
| # Function to plot, label, and save an image | |
| def plot_and_save_agent_image(agent_image, label, save_path=None): | |
| # Convert AgentImage to a raw PIL Image | |
| pil_image = agent_image.to_raw() | |
| # Add a label to the image | |
| labeled_image = add_label_to_image(pil_image, label) | |
| # Plot the image using PIL's show method | |
| labeled_image.show() | |
| # If save_path is provided, save the image | |
| if save_path: | |
| labeled_image.save(save_path) | |
| print(f"Image saved to {save_path}") | |
| else: | |
| print("No save path provided. Image not saved.") | |
| # Function to generate prompts for an object | |
| def generate_prompts_for_object(object_name): | |
| prompts = { | |
| "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." | |
| } | |
| return prompts | |
| # Function to generate the object's history images and GIF | |
| def generate_object_history(object_name): | |
| images = [] | |
| # Get prompts for the object | |
| prompts = generate_prompts_for_object(object_name) | |
| labels = { | |
| "past": f"{object_name} - Past", | |
| "present": f"{object_name} - Present", | |
| "future": f"{object_name} - Future" | |
| } | |
| # Generate sequential images and display them | |
| for time_period, frame in prompts.items(): | |
| print(f"Generating {time_period} frame: {frame}") | |
| result = agent.run(frame) # The tool generates the image | |
| # Append the image to the list for GIF creation | |
| images.append(result.to_raw()) # Ensure we're using raw image for GIF | |
| # Save each image with the appropriate name and label | |
| image_filename = f"{object_name}_{time_period}.png" | |
| plot_and_save_agent_image(result, labels[time_period], save_path=image_filename) | |
| # Create GIF from images | |
| gif_path = f"{object_name}_evolution.gif" | |
| images[0].save( | |
| gif_path, | |
| save_all=True, | |
| append_images=images[1:], | |
| duration=1000, # Duration in milliseconds for each frame | |
| loop=0 # Infinite loop | |
| ) | |
| # Return images and GIF path | |
| return images, gif_path | |
| #%% Initialization of tools and AI_Agent | |
| # Import text-to-image tool from Hub | |
| image_generation_tool = load_tool("m-ric/text-to-image", cache=False) | |
| # Import search tool from LangChain | |
| from transformers.agents.search import DuckDuckGoSearchTool | |
| search_tool = DuckDuckGoSearchTool() | |
| # Load the LLM engine | |
| llm_engine = HfApiEngine("Qwen/Qwen2.5-72B-Instruct") | |
| # Initialize the agent with both tools | |
| agent = ReactCodeAgent(tools=[image_generation_tool, search_tool], llm_engine=llm_engine) | |
| # Gradio interface | |
| def create_gradio_interface(): | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# TimeMetamorphy: an object Evolution Generator") | |
| # Add a section for instructions | |
| gr.Markdown(""" | |
| ## Unlocking the secrets of time! | |
| This app unveils these mysteries by offering a unique/magic lens that allows us "time travel". | |
| Powered by AI agents equipped with cutting-edge tools, it provides the superpower to explore the past, witness the present, and dream up the future like never before. | |
| This system allows you to generate visualizations of how an object/concept, like a bicycle or a car, may have evolved over time. | |
| It generates images of the object in the past, present, and future based on your input. | |
| ### Default Example: Evolution of a Car | |
| Below, you can see a precomputed example of a "car" evolution. Enter another object to generate its evolution. | |
| """) | |
| # Paths to the precomputed files | |
| default_images = [ | |
| ("car_past.png", "Car - Past"), | |
| ("car_present.png", "Car - Present"), | |
| ("car_future.png", "Car - Future") | |
| ] | |
| default_gif_path = "car_evolution.gif" | |
| with gr.Row(): | |
| with gr.Column(): | |
| # Textbox for user to input an object name | |
| object_name_input = gr.Textbox(label="Enter an object name (e.g., bicycle, phone)", | |
| placeholder="Enter an object name", | |
| lines=1) | |
| # Button to trigger the generation of images and GIF | |
| generate_button = gr.Button("Generate Evolution") | |
| # Gradio Gallery component to display the images | |
| image_gallery = gr.Gallery(label="Generated Images", show_label=True, columns=3, rows=1, value=default_images) | |
| # Output for the generated GIF | |
| gif_output = gr.Image(label="Generated GIF", show_label=True, value=default_gif_path) | |
| # Set the action when the button is clicked | |
| generate_button.click(fn=generate_object_history, inputs=[object_name_input], outputs=[image_gallery, gif_output]) | |
| return demo | |
| # Launch the Gradio app | |
| demo = create_gradio_interface() | |
| demo.launch(share=True) | |