Update app.py

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 import json
 import os
+import time  # For simulating progress
 
 # Paths
 image_folder = "Images/"  # Folder containing the images
@@ -10,32 +11,37 @@ metadata_file = "descriptions.json"  # JSON file with image descriptions
 with open(metadata_file, "r") as f:
     metadata = json.load(f)
 
-# Placeholder function for training LoRA
-def train_lora(image_folder, metadata):
-    # Prepare a dataset of image paths and descriptions
+# Placeholder function for training LoRA with progress tracking
+def train_lora_with_progress(image_folder, metadata, progress=gr.Progress()):
     dataset = []
+    num_images = len(metadata)
+    completed = 0
+
+    # Start processing images
     for image_name, description in metadata.items():
         image_path = os.path.join(image_folder, image_name)
         if os.path.exists(image_path):  # Ensure the image file exists
             dataset.append({"image": image_path, "description": description})
+            completed += 1
+            progress(completed / num_images, f"Processed {completed}/{num_images} images: {image_name}")
+            time.sleep(0.5)  # Simulating processing time
         else:
-            print(f"Warning: {image_name} not found in {image_folder}")
+            progress(completed / num_images, f"Warning: {image_name} not found in {image_folder}")
 
     # Placeholder for training logic
-    num_images = len(dataset)
-    return f"Training LoRA with {num_images} images and their descriptions."
+    return f"Training completed with {len(dataset)} valid images."
 
 # Define Gradio app
 def start_training():
-    return train_lora(image_folder, metadata)
+    return train_lora_with_progress(image_folder, metadata)
 
 # Gradio interface
 demo = gr.Interface(
     fn=start_training,
     inputs=None,
     outputs="text",
-    title="Train LoRA on Your Dataset",
-    description="Click below to start training with the uploaded images and metadata."
+    title="Train LoRA with Progress",
+    description="Click below to start training with the uploaded images and metadata. Progress will be displayed live."
 )
 
 demo.launch()