app.py

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 huggingface_hub import login
import os

token = os.environ.get("HF_TOKEN")
if token:
    login(token=token)
else:
    print("Warning: HF_TOKEN not set. You may not be able to access private models or tools.")


# =========================================================
# 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()
    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."
    }


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"
)


# =========================================================
# Tool and Agent Initialization
# =========================================================

search_tool = DuckDuckGoSearchTool()
#llm_engine = InferenceClientModel("Qwen/Qwen2.5-72B-Instruct")

llm_engine = InferenceClientModel("Qwen/Qwen2.5-Coder-32B-Instruct")
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_past.png",
            "car_present.png",
            "car_future.png"
        ]
        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, 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()