Update app.py

app.py

@@ -3,6 +3,7 @@ import os
 import subprocess
 import torch
 from PIL import Image
+import time
 
 UPLOAD_FOLDER = 'uploads'
 OUTPUT_FOLDER = 'outputs'
@@ -18,6 +19,7 @@ def gradio_interface(image):
     output_path = os.path.join(OUTPUT_FOLDER, "output.png")
 
     image.save(input_path)
+    print(f"Input image saved at: {input_path}")
 
     try:
         # Ensure CUDA memory is freed before running inference
@@ -30,22 +32,33 @@ def gradio_interface(image):
             "--input_path", input_path,
             "--output_path", output_path
         ]
+        
+        print("Running model...")
         result = subprocess.run(command, capture_output=True, text=True)
+        print("Model execution completed.")
+
+        # Log the output
+        print("STDOUT:", result.stdout)
+        print("STDERR:", result.stderr)
 
         if result.returncode != 0:
             return f"Error: {result.stderr}"
 
-        # Check if output image was created
+        # Wait for output file to be generated
+        time.sleep(2)  # Give time for file system updates
         if not os.path.exists(output_path):
             return "Error: Output image not generated."
 
-        # Explicitly reload the image
+        print(f"Output image generated: {output_path}")
+
+        # Explicitly reload the image to avoid caching issues
         return Image.open(output_path).copy()
 
     except Exception as e:
+        print(f"Exception occurred: {str(e)}")
         return f"Exception: {str(e)}"
 
-# Launch Gradio (without `share=True`)
+# Launch Gradio
 iface = gr.Interface(
     fn=gradio_interface,
     inputs=gr.Image(type="pil"),
@@ -53,4 +66,5 @@ iface = gr.Interface(
     title="Image Restoration with NAFNet"
 )
 
+print("Starting Gradio interface...")
 iface.launch()