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| 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 | |
| import Methods | |
| # 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) | |
| # 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. | |
| ### 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() | |
| # To make it permanent and hosted, we can use Gradio's 'share' argument or host it on a server. | |
| demo.launch(share=True) | |