Delete app_best.py

app_best.py

@@ -1,200 +0,0 @@
-#from transformers import load_tool, ReactCodeAgent, HfApiEngine
-from PIL import Image, ImageDraw, ImageFont
-import tempfile
-import gradio as gr
-from smolagents import CodeAgent, InferenceClientModel
-from smolagents import HfApiModel,  DuckDuckGoSearchTool
-#from transformers import load_tool
-
-#%% 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
-from smolagents.tools import Tool, load_tool
-
-'''
-image_generation_tool = Tool.from_space(
-    space_id="m-ric/text-to-image",
-    name="image_generator",
-    description="Generate an image from a prompt"
-)
-'''
-
-from smolagents import load_tool
-
-image_generation_tool = load_tool(
-    repo_id="m-ric/text-to-image",  # The Hugging Face Space repo
-    trust_remote_code=True,         
-    cache=False                     
-)
-
-'''
-class WrappedTextToImageTool(Tool):
-    name = "text_to_image"
-    description = "Generates an image from a text prompt using the m-ric/text-to-image tool."
-    inputs = ["prompt"]  # Optional: only if smolagents uses this
-
-    def run(self, prompt: str) -> str:
-        return image_generation_tool(prompt)
-
-image_generation_tool = WrappedTextToImageTool()
-'''
-
-
-# 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")
-#llm_engine =HfApiModel("Qwen/Qwen2.5-72B-Instruct")
-llm_engine= InferenceClientModel("Qwen/Qwen2.5-72B-Instruct")
-
-# Initialize the agent with both tools
-#agent = ReactCodeAgent(tools=[image_generation_tool, search_tool], llm_engine=llm_engine)
-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")
-        
-        # 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)