Create app.py

app.py

@@ -0,0 +1,133 @@
+from transformers import load_tool, ReactCodeAgent, HfApiEngine
+from PIL import Image
+import torch
+import numpy as np
+import tempfile
+import os
+import uuid
+import gradio as gr
+
+
+# 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 modern {object_name} with its current design and technology.",
+        "future": f"Show a futuristic version of a {object_name} with 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
+
+# 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.
+
+# Import search tool from LangChain
+#This tool allows the agent to search for and retrieve information from the web.
+from transformers.agents.search import DuckDuckGoSearchTool
+
+search_tool = DuckDuckGoSearchTool()
+
+# Qwen2.5-72B-Instruct is a specific, a LLM fine-tuned for instruction-following tasks.
+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)
+
+
+# Your existing generate_object_history function goes here
+
+# Gradio interface
+def create_gradio_interface():
+    with gr.Blocks() as demo:
+        gr.Markdown("# Object Evolution Generator")
+        
+        # Add a section for instructions
+        gr.Markdown("""
+        ## Welcome to the Object Evolution Generator!
+
+        This app allows you to generate visualizations of how an object, 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.
+
+        ### How to use:
+        - Enter the name of an object (e.g., "bicycle", "car", "phone").
+        - Click "Generate Evolution" to generate the evolution of the object across three time periods: past, present, and future.
+        - View the generated images and a GIF showing the evolution of the object.
+
+        ### Example:
+        Try entering an object name like "car" and see how it has evolved!
+        """)
+
+        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., car)", 
+                                              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)
+
+                # Output for the generated GIF
+                gif_output = gr.Image(label="Generated GIF", show_label=True)
+         
+        # 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 (permanently)
+demo = create_gradio_interface()
+
+# To make it permanent and hosted, we can use Gradio's 'share' argument or host it on a server.
+demo.launch(share=True)