Create app.py

app.py

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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."
+
+    def __init__(self):
+        super().__init__()
+        self.client = Client("m-ric/text-to-image")  # Calls HF Space
+
+    def run(self, prompt: str):
+        image = self.client.predict(prompt, api_name="/predict")
+        return image  # This is a 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)