Create app_init.py

app_init.py

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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)