Update app.py

app.py

@@ -1,3 +1,4 @@
+#%% Import libraries
 from transformers import load_tool, ReactCodeAgent, HfApiEngine
 from PIL import Image
 import torch
@@ -6,9 +7,69 @@ import tempfile
 import os
 import uuid
 import gradio as gr
-from Methods import *
 
 
+#%% Methods 
+# function to plot and save an AgentImage
+def plot_and_save_agent_image(agent_image, save_path=None):
+    # Convert AgentImage to a raw PIL Image
+    pil_image = agent_image.to_raw()
+
+    # Plot the image using PIL's show method
+    pil_image.show()
+
+    # If save_path is provided, save the image
+    if save_path:
+        pil_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):
+    prompts = {
+        "past": f"Show an old version of a {object_name} from its early days.",
+        "present": f"Show a {object_name} with from present 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 car industry history
+def generate_object_history(object_name):
+    images = []
+    
+    # Get prompts for the object
+    prompts = generate_prompts_for_object(object_name)
+    
+    # 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 (past, present, future)
+        image_filename = f"{object_name}_{time_period}.png"
+        plot_and_save_agent_image(result, 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 
 # m-ric/text-to-image model generates images based on textual descriptions.
 image_generation_tool = load_tool("m-ric/text-to-image", cache=False) #cache=False ensures it fetches the latest tool updates directly from the Hub.