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from PIL import Image, ImageDraw, ImageFont
import gradio as gr
from smolagents import CodeAgent, InferenceClientModel, DuckDuckGoSearchTool, Tool
from gradio_client import Client
#%% Tool Wrapper for the Hugging Face Space
class TextToImageTool(Tool):
name = "text_to_image"
description = "Generate an image from a text prompt using m-ric/text-to-image."
# `inputs` is a dictionary: argument_name -> type
inputs = {"prompt": str}
output_type = "image"
outputs = {"image": "image"} # Optional, type can be a string or class
def __init__(self):
super().__init__()
self.client = Client("m-ric/text-to-image")
def run(self, prompt: str):
return self.client.predict(prompt, api_name="/predict") # Returns PIL.Image
#%% 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_bbox[2] - text_bbox[0]
text_height = 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(image, label, save_path=None):
labeled_image = add_label_to_image(image, label)
labeled_image.show()
if save_path:
labeled_image.save(save_path)
print(f"Image saved to {save_path}")
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."
}
def generate_object_history(object_name):
prompts = generate_prompts_for_object(object_name)
labels = {
"past": f"{object_name} - Past",
"present": f"{object_name} - Present",
"future": f"{object_name} - Future"
}
images = []
for time_period, prompt in prompts.items():
print(f"Generating {time_period} frame: {prompt}")
result = agent.run(prompt) # Runs tool
if hasattr(result, "to_raw"): # If wrapped output
result = result.to_raw()
images.append(result)
plot_and_save_agent_image(result, labels[time_period], save_path=f"{object_name}_{time_period}.png")
gif_path = f"{object_name}_evolution.gif"
images[0].save(gif_path, save_all=True, append_images=images[1:], duration=1000, loop=0)
return images, gif_path
#%% Tool & Agent Setup
image_generation_tool = TextToImageTool()
search_tool = DuckDuckGoSearchTool()
llm_engine = InferenceClientModel("Qwen/Qwen2.5-72B-Instruct")
agent = CodeAgent(tools=[image_generation_tool, search_tool], model=llm_engine)
#%% Gradio Interface
def create_gradio_interface():
with gr.Blocks() as demo:
gr.Markdown("# TimeMetamorphy: an object Evolution Generator")
gr.Markdown("""
## Unlocking the secrets of time!
Enter an object name (like bicycle or smartphone), and this app will generate its visual evolution.
""")
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():
object_name_input = gr.Textbox(label="Enter an object name", placeholder="e.g., bicycle, phone")
generate_button = gr.Button("Generate Evolution")
image_gallery = gr.Gallery(label="Generated Images", columns=3, rows=1, value=default_images)
gif_output = gr.Image(label="Generated GIF", value=default_gif_path)
generate_button.click(fn=generate_object_history, inputs=[object_name_input], outputs=[image_gallery, gif_output])
return demo
# Launch app
demo = create_gradio_interface()
demo.launch(share=True)
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