Create app.py

app.py

@@ -1,7 +1,41 @@
 import gradio as gr
+import json
+import os
 
-def greet(name):
-    return "Hello " + name + "!!"
+# 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
+def train_lora(image_folder, metadata):
+    # Prepare a dataset of image paths and descriptions
+    dataset = []
+    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})
+        else:
+            print(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."
+
+# Define Gradio app
+def start_training():
+    return train_lora(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."
+)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
 demo.launch()