Delete apps/old-gradio_app.py

apps/old-gradio_app.py

@@ -1,136 +0,0 @@
-import gradio as gr
-import os
-import sys
-import json
-from PIL import Image
-
-from gradio_app.project_info import (
-    CONTENT_DESCRIPTION,
-    CONTENT_IN_1, CONTENT_IN_2,
-    CONTENT_OUT_1, CONTENT_OUT_2
-)
-# Add the project root directory to the Python path
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-
-from src.anime_super_resolution.infer import infer
-
-def run_inference(input_image, model_id, outer_scale):
-    if not input_image:
-        return None, "Please upload an image."
-    
-    models_config_path = "configs/model_ckpts.yaml"
-    try:
-        output_image = infer(
-            input_path=input_image,
-            model_id=model_id,
-            models_config_path=models_config_path,
-            outer_scale=outer_scale,
-        )
-        return output_image, "Inference completed successfully!"
-    except Exception as e:
-        return None, f"Error during inference: {str(e)}"
-
-def load_examples():
-    examples = []
-    examples_base_path = os.path.join("apps", "assets", "examples", "Real-ESRGAN-Anime-finetuning")
-    
-    for folder in ["1", "2", "3", "4"]:
-        folder_path = os.path.join(examples_base_path, folder)
-        config_path = os.path.join(folder_path, "config.json")
-        
-        if os.path.exists(config_path):
-            with open(config_path, 'r') as f:
-                config = json.load(f)
-                input_filename = config.get("input_file", "input.jpg")
-                output_filename = config.get("output_file", "output.jpg")
-                outer_scale = config.get("outer_scale", 4)
-                
-                input_image_path = os.path.join(folder_path, input_filename)
-                output_image_path = os.path.join(folder_path, output_filename)
-                
-                if os.path.exists(input_image_path) and os.path.exists(output_image_path):
-                    input_image_data = Image.open(input_image_path)
-                    output_image_data = Image.open(output_image_path)
-                    examples.append([input_image_data, output_image_data, outer_scale])
-    
-    return examples
-
-def select_example(evt: gr.SelectData, examples_data):
-    example_index = evt.index
-    input_image_data, output_image_data, outer_scale = examples_data[example_index]
-    return input_image_data, outer_scale, output_image_data, f"Loaded example with Outer Scale: {outer_scale}"
-
-# Load custom CSS
-custom_css = open("apps/gradio_app/static/styles.css").read()
-
-# JavaScript function to update warning_text Markdown component
-outer_scale_warning = open("apps/gradio_app/static/outer_scale_warning.js").read()
-
-# Define Gradio interface
-with gr.Blocks(css=custom_css) as demo:
-    gr.Markdown("# Anime Super Resolution 🖼️")
-    gr.Markdown(CONTENT_DESCRIPTION)
-    gr.Markdown(CONTENT_IN_1)
-    gr.HTML(CONTENT_IN_2)
-    with gr.Row():
-        with gr.Column(scale=2):
-            input_image = gr.Image(
-                type="filepath",
-                label="Input Image",
-                elem_classes="input-image"
-            )
-            model_id = gr.Textbox(
-                label="Model ID",
-                value="danhtran2mind/Real-ESRGAN-Anime-finetuning"
-            )
-            
-            outer_scale = gr.Slider(
-                minimum=1,
-                maximum=8,
-                step=1,
-                value=2,
-                label="Outer Scale",
-                elem_id="outer-scale-slider"
-            )
-            warning_text = gr.HTML(elem_id="warning-text")  # Added elem_id for JS targeting
-            gr.Markdown(
-                "**Note:** For optimal output quality, set `Outer Scale` to a value between 1 and 4. "
-                "**Values greater than 4 are not recommended**. "
-                "Please ensure `Outer Scale` is greater than or equal to `Inner Scale` (default: 4)."
-            )
-            
-            examples_data = load_examples()
-            submit_button = gr.Button("Run Inference")
-        
-        with gr.Column(scale=3):
-            output_image = gr.Image(
-                label="Output Image",
-                elem_classes="output-image"
-            )
-            output_text = gr.Textbox(label="Status")
-    
-    # Client-side warning update for warning_text
-    outer_scale.change(
-        fn=lambda x: x,  # Pass-through function to satisfy Gradio
-        inputs=outer_scale,
-        outputs=outer_scale,  # Return value to maintain slider functionality
-        js=outer_scale_warning  # Update warning_text via JavaScript
-    )
-    
-    gr.Examples(
-        examples=[[input_img, output_img, outer_scale] for input_img, output_img, outer_scale in examples_data],
-        inputs=[input_image, output_image, outer_scale],
-        label="Example Inputs",
-        examples_per_page=4
-    )
-        
-    submit_button.click(
-        fn=run_inference,
-        inputs=[input_image, model_id, outer_scale],
-        outputs=[output_image, output_text]
-    )
-    gr.HTML(CONTENT_OUT_1)
-    gr.Markdown(CONTENT_OUT_2)
-
-if __name__ == "__main__":
-    demo.launch(share=True)  # Changed to local launch for safety; use share=True for public URL if needed