Update app.py

app.py

@@ -7,37 +7,25 @@ Original file is located at
     https://colab.research.google.com/drive/1_wYfP0IRdb9fpc2zvbg8IqdXGx1dTo7X
 """
 
-
 from datasets import load_dataset
 from PIL import Image
-
-# Load dataset from Hugging Face
-dataset = load_dataset("lirus18/deepfashion", split="train")
-
-from PIL import Image
-
-# Just to verify image loaded (not for UI)
-image = dataset[0]['image']
-image.save("sample_image.jpg")  # saves one image to disk
-
-
 from transformers import CLIPProcessor, CLIPModel
+from sklearn.metrics.pairwise import cosine_similarity
 import torch
+import numpy as np
+import gradio as gr
 
-# Load the CLIP model and processor
+# Load dataset and model
+dataset = load_dataset("lirus18/deepfashion", split="train")
 model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
 processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
 
-# Use GPU if available
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model.to(device)
 
-import numpy as np
-
+# Precompute vectors
 image_vectors = []
 image_indices = []
-
-# Use a subset (you can increase to 1000+ later)
 N = 500
 
 for i in range(N):
@@ -46,45 +34,25 @@ for i in range(N):
     with torch.no_grad():
         embedding = model.get_image_features(**inputs)
     image_vectors.append(embedding.cpu().numpy().squeeze())
-    image_indices.append(i)  # Store index for later retrieval
+    image_indices.append(i)
 
 image_vectors = np.array(image_vectors)
 
-from sklearn.metrics.pairwise import cosine_similarity
-
 def find_similar(user_image, top_k=3, exclude_index=None):
-    # Embed the user image
     inputs = processor(images=user_image.convert("RGB"), return_tensors="pt").to(device)
     with torch.no_grad():
         query_vec = model.get_image_features(**inputs).cpu().numpy()
 
-    # Compute cosine similarity
     sims = cosine_similarity(query_vec, image_vectors)[0]
-
-    # Exclude the query image itself
     if exclude_index is not None:
-        sims[exclude_index] = -1  # Force low similarity
+        sims[exclude_index] = -1
 
-    # Get top K similar image indices
     top_idx = sims.argsort()[-top_k:][::-1]
-
     return [dataset[image_indices[i]]['image'] for i in top_idx]
 
-from IPython.display import display
-
-query_index = 10
-query_image = dataset[query_index]['image']
-display(query_image)
-
-similar_images = find_similar(query_image, exclude_index=query_index)
-
-for img in similar_images:
-    display(img)
-
+# Placeholder for Stable Diffusion (optional)
 from diffusers import StableDiffusionImg2ImgPipeline
-import torch
 
-# Load Stable Diffusion (this might take a while)
 pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5",
     torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
@@ -99,7 +67,6 @@ def generate_outfit_from_image(input_image):
 from PIL import ImageChops
 
 def recommend_from_upload(uploaded_image):
-    # Step 1: Compare uploaded image to all dataset images and find the most identical one
     uploaded_image = uploaded_image.convert("RGB")
     closest_idx = None
     for i in range(len(image_indices)):
@@ -108,25 +75,12 @@ def recommend_from_upload(uploaded_image):
             closest_idx = i
             break
 
-    # Step 2: Get top 3 similar, excluding the identical one if found
     similar_imgs = find_similar(uploaded_image, top_k=3, exclude_index=closest_idx)
-
-    # Step 3: Generate 1 new outfit (placeholder for now)
     generated_img = generate_outfit_from_image(uploaded_image)
 
     return [uploaded_image] + similar_imgs + [generated_img]
 
-import gradio as gr
-import os
-
-# Prepare example paths
-example_images = [
-    ["/content/fashion_examples/new1.jpg"],
-    ["/content/fashion_examples/newnew.jpg"],
-    ["/content/fashion_examples/newoutfit.jpg"],
-]
-
-# Build the interface
+# Gradio Interface
 demo = gr.Interface(
     fn=recommend_from_upload,
     inputs=gr.Image(type="pil", label="Upload a clothing item"),
@@ -138,8 +92,7 @@ demo = gr.Interface(
         gr.Image(label="Generated New Outfit"),
     ],
     title="👗 Fashion Outfit Recommender",
-    description="Upload your own image *or* click an example to get 3 similar items + 1 AI-generated outfit.",
-    examples=example_images
+    description="Upload a clothing image to see 3 similar outfits and 1 AI-generated one!"
 )
 
-demo.launch()
+demo.launch()