Update app.py

app.py

@@ -14,7 +14,6 @@ from sklearn.metrics.pairwise import cosine_similarity
 import torch
 import numpy as np
 import gradio as gr
-from diffusers import StableDiffusionImg2ImgPipeline
 
 # Device setup
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -29,7 +28,7 @@ dataset = load_dataset("lirus18/deepfashion", split="train")
 # Embed a subset of dataset images
 image_vectors = []
 image_indices = []
-N = 500
+N = 500  # You can increase this later if needed
 
 for i in range(N):
     img = dataset[i]['image'].convert("RGB")
@@ -52,33 +51,13 @@ def find_similar(user_image, top_k=3, exclude_index=None):
         sims[exclude_index] = -1
 
     top_idx = sims.argsort()[-top_k:][::-1]
-    return [dataset[image_indices[i]]['image'] for i in top_idx], query_vec
+    return [dataset[image_indices[i]]['image'] for i in top_idx]
 
-# Load Stable Diffusion
-pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5",
-    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-    low_cpu_mem_usage=True
-).to(device)
-pipe.enable_attention_slicing()
-
-# 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 = []
-
-    for _ in range(n):
-        result = pipe(prompt=prompt, image=init_image, strength=0.7, guidance_scale=7.5)
-        generated_images.append(result.images[0])
-
-    return generated_images
-
-# Main recommendation function
+# Main function (no generation, just simulation)
 def recommend_from_upload(uploaded_image):
     uploaded_image = uploaded_image.convert("RGB")
 
-    # Check if the uploaded image exists in dataset
+    # Check if uploaded image is already in dataset
     closest_idx = None
     for i in range(len(image_indices)):
         dataset_image = dataset[image_indices[i]]['image'].convert("RGB")
@@ -86,27 +65,15 @@ def recommend_from_upload(uploaded_image):
             closest_idx = i
             break
 
-    # Get 3 similar items + embedding
-    similar_imgs, query_vec = find_similar(uploaded_image, top_k=3, exclude_index=closest_idx)
-
-    # Generate 2 synthetic outfits
-    generated_imgs = generate_outfits(uploaded_image, n=2)
+    # Get 3 similar images
+    similar_imgs = find_similar(uploaded_image, top_k=3, exclude_index=closest_idx)
 
-    # Select most similar generated image
-    best_score = -1
-    best_generated_img = None
-    for img in generated_imgs:
-        inputs = processor(images=img, return_tensors="pt").to(device)
-        with torch.no_grad():
-            emb = model.get_image_features(**inputs).cpu().numpy()
-        sim = cosine_similarity(query_vec, emb)[0][0]
-        if sim > best_score:
-            best_score = sim
-            best_generated_img = img
+    # Use a gray image as the "best generated outfit"
+    placeholder_img = Image.new("RGB", (512, 512), color="gray")
 
-    return [uploaded_image] + similar_imgs + [best_generated_img]
+    return [uploaded_image] + similar_imgs + [placeholder_img]
 
-# Example image paths (must exist in root folder)
+# Example image paths (must be in root folder)
 example_paths = [
     ["example1.jpg"],
     ["example2.jpg"],
@@ -115,10 +82,10 @@ example_paths = [
     ["example5.jpg"]
 ]
 
-# Gradio Interface with button
+# 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.")
+    gr.Markdown("Upload a clothing image to get 3 similar items from the dataset and 1 simulated AI-generated outfit.")
 
     with gr.Row():
         image_input = gr.Image(type="pil", label="Upload a clothing item")
@@ -130,7 +97,7 @@ with gr.Blocks() as demo:
         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")
+        output5 = gr.Image(label="Simulated AI-Generated Outfit")
 
     examples = gr.Examples(
         examples=example_paths,
@@ -145,3 +112,4 @@ with gr.Blocks() as demo:
 if __name__ == "__main__":
     demo.launch()
 
+