Update app.py

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 import json
 import os
-import time
 
 # Paths
 image_folder = "Images/"  # Folder containing the images
@@ -10,32 +9,44 @@ metadata_file = "descriptions.json"  # JSON file with image descriptions
 # Load metadata
 with open(metadata_file, "r") as f:
     metadata = json.load(f)
+    print(f"Loaded metadata: {len(metadata)} items")  # Print the number of descriptions
 
-# Function for training with simple console logging
-def train_lora_with_progress():
+# Placeholder function for training LoRA
+def train_lora(image_folder, metadata):
+    print("Starting training process...")  # Log the start of the training
+    # Prepare a dataset of image paths and descriptions
     dataset = []
-    num_images = len(metadata)
-    progress_log = ""
-    
-    # Process images and descriptions
-    for i, (image_name, description) in enumerate(metadata.items()):
+    for image_name, description in metadata.items():
         image_path = os.path.join(image_folder, image_name)
-        if os.path.exists(image_path):
+        if os.path.exists(image_path):  # Ensure the image file exists
             dataset.append({"image": image_path, "description": description})
-            progress_log += f"Processed {i+1}/{num_images}: {image_name}\n"
+            print(f"Added {image_name} to dataset")  # Log each added image
         else:
-            progress_log += f"Warning: {image_name} not found.\n"
-        time.sleep(0.5)  # Simulate time for each step
+            print(f"Warning: {image_name} not found in {image_folder}")  # Log missing images
+
+    # Log how many images were successfully added
+    num_images = len(dataset)
+    print(f"Dataset prepared with {num_images} images.")
     
-    return progress_log + f"\nTraining completed with {len(dataset)} valid images."
+    # Placeholder for training logic
+    # Replace this with your actual training code
+    print("Training LoRA with the prepared dataset...")
+    
+    # For now, just return a message
+    return f"Training LoRA with {num_images} images and their descriptions."
+
+# Define Gradio app
+def start_training():
+    return train_lora(image_folder, metadata)
 
-# Gradio app
+# Gradio interface
 demo = gr.Interface(
-    fn=train_lora_with_progress,
+    fn=start_training,
     inputs=None,
     outputs="text",
-    title="Train LoRA with Progress Log",
-    description="Click below to start training and view live progress logs."
+    title="Train LoRA on Your Dataset",
+    description="Click below to start training with the uploaded images and metadata."
 )
 
-demo.launch(enable_queue=True)
+# Launch the Gradio interface
+demo.launch()