Update app.py

app.py

@@ -1,52 +1,52 @@
-import gradio as gr
-import os
-import subprocess
-import torch
-from PIL import Image
-import numpy as np
-
-UPLOAD_FOLDER = 'uploads'
-OUTPUT_FOLDER = 'outputs'
-
-os.makedirs(UPLOAD_FOLDER, exist_ok=True)
-os.makedirs(OUTPUT_FOLDER, exist_ok=True)
-
-# Fix CUDA Out of Memory issue by enabling memory management
-os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
-
-def gradio_interface(image):
-    input_path = os.path.join(UPLOAD_FOLDER, "input.png")
-    output_path = os.path.join(OUTPUT_FOLDER, "output.png")
-
-    image.save(input_path)
-
-    try:
-        # Ensure CUDA memory is freed before running inference
-        torch.cuda.empty_cache()
-
-        # Run the NAFNet model with controlled memory usage
-        command = [
-            "python", "NAFNet/demo.py",
-            "-opt", "NAFNet/options/test/REDS/NAFNet-width64.yml",
-            "--input_path", input_path,
-            "--output_path", output_path
-        ]
-        result = subprocess.run(command, capture_output=True, text=True)
-
-        if result.returncode != 0:
-            return f"Error: {result.stderr}"
-
-        return Image.open(output_path)
-
-    except Exception as e:
-        return f"Exception: {str(e)}"
-
-# Launch Gradio with public link
-iface = gr.Interface(
-    fn=gradio_interface,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Image(type="pil"),
-    title="Image Restoration with NAFNet"
-)
-
-iface.launch(share=True)
+import gradio as gr
+import os
+import subprocess
+import torch
+from PIL import Image
+import numpy as np
+
+UPLOAD_FOLDER = 'uploads'
+OUTPUT_FOLDER = 'outputs'
+
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+os.makedirs(OUTPUT_FOLDER, exist_ok=True)
+
+# Fix CUDA Out of Memory issue by enabling memory management
+os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
+
+def gradio_interface(image):
+    input_path = os.path.join(UPLOAD_FOLDER, "input.png")
+    output_path = os.path.join(OUTPUT_FOLDER, "output.png")
+
+    image.save(input_path)
+
+    try:
+        # Ensure CUDA memory is freed before running inference
+        torch.cuda.empty_cache()
+
+        # Run the NAFNet model with controlled memory usage
+        command = [
+            "python", "NAFNet/demo.py",
+            "-opt", "NAFNet/options/test/REDS/NAFNet-width64.yml",
+            "--input_path", input_path,
+            "--output_path", output_path
+        ]
+        result = subprocess.run(command, capture_output=True, text=True)
+
+        if result.returncode != 0:
+            return f"Error: {result.stderr}"
+
+        return Image.open(output_path)
+
+    except Exception as e:
+        return f"Exception: {str(e)}"
+
+# Launch Gradio (without `share=True`)
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Image(type="pil"),
+    title="Image Restoration with NAFNet"
+)
+
+iface.launch()  # Removed share=True