Update app.py

app.py

@@ -29,7 +29,7 @@ dataset = load_dataset("lirus18/deepfashion", split="train")
 # Embed a subset of dataset images
 image_vectors = []
 image_indices = []
-N = 500  # You can increase this if performance is okay
+N = 500
 
 for i in range(N):
     img = dataset[i]['image'].convert("RGB")
@@ -62,7 +62,8 @@ pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
 ).to(device)
 pipe.enable_attention_slicing()
 
-def generate_outfits(input_image, n=10):
+# Generate outfits (2 only)
+def generate_outfits(input_image, n=2):
     prompt = "fashion outfit design inspired by the clothing item"
     init_image = input_image.resize((512, 512))
     generated_images = []
@@ -73,11 +74,11 @@ def generate_outfits(input_image, n=10):
 
     return generated_images
 
-# Main function
+# Main recommendation function
 def recommend_from_upload(uploaded_image):
     uploaded_image = uploaded_image.convert("RGB")
 
-    # Check if the uploaded image already exists in the dataset
+    # Check if the uploaded image exists in dataset
     closest_idx = None
     for i in range(len(image_indices)):
         dataset_image = dataset[image_indices[i]]['image'].convert("RGB")
@@ -85,13 +86,13 @@ def recommend_from_upload(uploaded_image):
             closest_idx = i
             break
 
-    # Find 3 similar dataset items + get uploaded image embedding
+    # Get 3 similar items + embedding
     similar_imgs, query_vec = find_similar(uploaded_image, top_k=3, exclude_index=closest_idx)
 
-    # Generate 10 new outfit images
-    generated_imgs = generate_outfits(uploaded_image, n=10)
+    # Generate 2 synthetic outfits
+    generated_imgs = generate_outfits(uploaded_image, n=2)
 
-    # Find the most relevant generated image
+    # Select most similar generated image
     best_score = -1
     best_generated_img = None
     for img in generated_imgs:
@@ -103,10 +104,9 @@ def recommend_from_upload(uploaded_image):
             best_score = sim
             best_generated_img = img
 
-    # Final output: input + 3 similar + 1 most relevant generated
     return [uploaded_image] + similar_imgs + [best_generated_img]
 
-# Example images (make sure they are in the root folder)
+# Example image paths (must exist in root folder)
 example_paths = [
     ["example1.jpg"],
     ["example2.jpg"],
@@ -115,23 +115,33 @@ example_paths = [
     ["example5.jpg"]
 ]
 
-# Gradio Interface
-demo = gr.Interface(
-    fn=recommend_from_upload,
-    inputs=gr.Image(type="pil", label="Upload a clothing item"),
-    outputs=[
-        gr.Image(label="Your Input"),
-        gr.Image(label="Similar Item 1"),
-        gr.Image(label="Similar Item 2"),
-        gr.Image(label="Similar Item 3"),
-        gr.Image(label="Best AI-Generated Outfit")
-    ],
-    title="👗 Fashion Outfit Recommender",
-    description="Upload a clothing image to get 3 similar items from the dataset and 1 best AI-generated outfit design.",
-    examples=example_paths
-)
+# Gradio Interface with button
+with gr.Blocks() as demo:
+    gr.Markdown("## 👗 Fashion Outfit Recommender")
+    gr.Markdown("Upload a clothing image to get 3 similar items from the dataset and 1 AI-generated outfit design.")
+
+    with gr.Row():
+        image_input = gr.Image(type="pil", label="Upload a clothing item")
+
+    generate_btn = gr.Button("Generate Recommendations")
+
+    with gr.Row():
+        output1 = gr.Image(label="Your Input")
+        output2 = gr.Image(label="Similar Item 1")
+        output3 = gr.Image(label="Similar Item 2")
+        output4 = gr.Image(label="Similar Item 3")
+        output5 = gr.Image(label="Best AI-Generated Outfit")
+
+    examples = gr.Examples(
+        examples=example_paths,
+        inputs=image_input,
+        label="Try an Example"
+    )
+
+    generate_btn.click(fn=recommend_from_upload,
+                       inputs=image_input,
+                       outputs=[output1, output2, output3, output4, output5])
 
 if __name__ == "__main__":
     demo.launch()
 
-