Delete app.py

app.py

@@ -1,47 +0,0 @@
-import gradio as gr
-import json
-import os
-import time  # For simulating progress
-
-# 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)
-
-# 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:
-            progress(completed / num_images, f"Warning: {image_name} not found in {image_folder}")
-
-    # Placeholder for training logic
-    return f"Training completed with {len(dataset)} valid images."
-
-# Define Gradio app
-def start_training():
-    return train_lora_with_progress(image_folder, metadata)
-
-# Gradio interface
-demo = gr.Interface(
-    fn=start_training,
-    inputs=None,
-    outputs="text",
-    title="Train LoRA with Progress",
-    description="Click below to start training with the uploaded images and metadata. Progress will be displayed live."
-)
-
-demo.launch()