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apps/gradio_app.py

@@ -1,97 +1,97 @@
-import gradio as gr
-import os
-import subprocess
-from gradio_app.project_info import (
-    CONTENT_DESCRIPTION,
-    CONTENT_IN_1, CONTENT_IN_2,
-    CONTENT_OUT_1, CONTENT_OUT_2
-)
-from gradio_app.inference import run_inference
-from gradio_app.examples import load_examples, select_example
-
-
-def run_setup_script():
-    setup_script = os.path.join(os.path.dirname(__file__),
-                                "gradio_app", "setup_scripts.py")
-    try:
-        result = subprocess.run(["python", setup_script], capture_output=True, text=True, check=True)
-        return result.stdout
-    except subprocess.CalledProcessError as e:
-        print(f"Setup script failed with error: {e.stderr}")
-        return f"Setup script failed: {e.stderr}"
-    
-def create_gui():
-    # Load CSS and JavaScript
-    custom_css = open("apps/gradio_app/static/styles.css").read()
-    outer_scale_warning = open("apps/gradio_app/static/outer_scale_warning.js").read()
-
-    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")
-                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")
-        
-        outer_scale.change(
-            fn=lambda x: x,
-            inputs=outer_scale,
-            outputs=outer_scale,
-            js=outer_scale_warning
-        )
-        
-        gr.Examples(
-            examples=[[input_img, output_img, outer_scale] for input_img, output_img, outer_scale in examples_data],
-            inputs=[input_image, outer_scale],  # Corrected inputs
-            label="Example Inputs",
-            examples_per_page=4,
-            fn=select_example,
-            outputs=[input_image, outer_scale, output_image, output_text]
-        )
-            
-        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)
-
-    return demo
-    
-if __name__ == "__main__":
-    run_setup_script()
-    demo = create_gui()
+import gradio as gr
+import os
+import subprocess
+from gradio_app.project_info import (
+    CONTENT_DESCRIPTION,
+    CONTENT_IN_1, CONTENT_IN_2,
+    CONTENT_OUT_1, CONTENT_OUT_2
+)
+from gradio_app.inference import run_inference
+from gradio_app.examples import load_examples, select_example
+
+def run_setup_script():
+    setup_script = os.path.join(os.path.dirname(__file__), "gradio_app", "setup_scripts.py")
+    try:
+        result = subprocess.run(["python", setup_script], capture_output=True, text=True, check=True)
+        return result.stdout
+    except subprocess.CalledProcessError as e:
+        print(f"Setup script failed with error: {e.stderr}")
+        return f"Setup script failed: {e.stderr}"
+
+def create_gui():
+    # Load CSS and JavaScript
+    custom_css = open(os.path.join(os.path.dirname(__file__), "gradio_app", "static", "styles.css")).read()
+    outer_scale_warning = open(os.path.join(os.path.dirname(__file__), "gradio_app", "static", "outer_scale_warning.js")).read()
+
+    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")
+                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()
+                if not examples_data:
+                    gr.Warning("No examples found. Check the 'apps/assets/examples' directory.")
+                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")
+
+        outer_scale.change(
+            fn=lambda x: x,
+            inputs=outer_scale,
+            outputs=outer_scale,
+            js=outer_scale_warning
+        )
+
+        gr.Examples(
+            examples=[[input_img, output_img, outer_scale] for input_img, output_img, outer_scale in examples_data],
+            inputs=[input_image, outer_scale],
+            label="Example Inputs",
+            examples_per_page=4,
+            fn=select_example,
+            outputs=[input_image, outer_scale, output_image, output_text]
+        )
+
+        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)
+
+    return demo
+
+if __name__ == "__main__":
+    run_setup_script()
+    demo = create_gui()
     demo.launch(debug=True)

apps/gradio_app/examples.py

@@ -5,30 +5,37 @@ import gradio as gr
 
 def load_examples():
     examples = []
-    examples_base_path = os.path.join("apps", "assets", "examples", "Real-ESRGAN-Anime-finetuning")
+    examples_base_path = os.path.join(os.path.dirname(__file__), "..", "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])
+        try:
+            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):
+                        # Verify images can be opened
+                        Image.open(input_image_path).close()
+                        Image.open(output_image_path).close()
+                        examples.append([input_image_path, output_image_path, outer_scale])
+        except (json.JSONDecodeError, OSError) as e:
+            print(f"Error loading example in folder {folder}: {e}")
+            continue
     
     return examples
 
 def select_example(evt: gr.SelectData, examples_data):
+    if not examples_data:
+        return None, 2, None, "No examples available"
     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}"
+    input_image_path, output_image_path, outer_scale = examples_data[example_index]
+    return input_image_path, outer_scale, output_image_path, f"Loaded example with Outer Scale: {outer_scale}"