Create app.py

app.py

@@ -0,0 +1,41 @@
+import gradio as gr
+import json
+import os
+import time
+
+# Paths
+image_folder = "Images/"  # Folder containing the images
+metadata_file = "descriptions.json"  # JSON file with image descriptions
+
+# Load metadata
+with open(metadata_file, "r") as f:
+    metadata = json.load(f)
+
+# Function for training with simple console logging
+def train_lora_with_progress():
+    dataset = []
+    num_images = len(metadata)
+    progress_log = ""
+    
+    # Process images and descriptions
+    for i, (image_name, description) in enumerate(metadata.items()):
+        image_path = os.path.join(image_folder, image_name)
+        if os.path.exists(image_path):
+            dataset.append({"image": image_path, "description": description})
+            progress_log += f"Processed {i+1}/{num_images}: {image_name}\n"
+        else:
+            progress_log += f"Warning: {image_name} not found.\n"
+        time.sleep(0.5)  # Simulate time for each step
+    
+    return progress_log + f"\nTraining completed with {len(dataset)} valid images."
+
+# Gradio app
+demo = gr.Interface(
+    fn=train_lora_with_progress,
+    inputs=None,
+    outputs="text",
+    title="Train LoRA with Progress Log",
+    description="Click below to start training and view live progress logs."
+)
+
+demo.launch(enable_queue=True)