app.py

# -*- coding: utf-8 -*-
"""FinalProject.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1_wYfP0IRdb9fpc2zvbg8IqdXGx1dTo7X
"""

from datasets import load_dataset
from PIL import Image, ImageChops
from transformers import CLIPProcessor, CLIPModel
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"

# Load CLIP model
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device)
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")

# Load dataset
dataset = load_dataset("lirus18/deepfashion", split="train")

# Embed a subset of images
image_vectors = []
image_indices = []
N = 500

for i in range(N):
    img = dataset[i]['image'].convert("RGB")
    inputs = processor(images=img, return_tensors="pt").to(device)
    with torch.no_grad():
        emb = model.get_image_features(**inputs)
    image_vectors.append(emb.cpu().numpy().squeeze())
    image_indices.append(i)

image_vectors = np.array(image_vectors)

# Find similar images
def find_similar(user_image, top_k=3, exclude_index=None):
    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()

    sims = cosine_similarity(query_vec, image_vectors)[0]
    if exclude_index is not None:
        sims[exclude_index] = -1

    top_idx = sims.argsort()[-top_k:][::-1]
    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()

def generate_outfits(input_image, n=10):
    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

def recommend_from_upload(uploaded_image):
    uploaded_image = uploaded_image.convert("RGB")
    closest_idx = None
    for i in range(len(image_indices)):
        dataset_image = dataset[image_indices[i]]['image'].convert("RGB")
        if ImageChops.difference(dataset_image, uploaded_image).getbbox() is None:
            closest_idx = i
            break

    similar_imgs = find_similar(uploaded_image, top_k=3, exclude_index=closest_idx)
    generated_imgs = generate_outfits(uploaded_image, n=10)

    return [uploaded_image] + similar_imgs + generated_imgs

# 5 clickable example images (must be uploaded to the repo)
example_paths = [
    ["fashion_examples/example1.jpg"],
    ["fashion_examples/example2.jpg"],
    ["fashion_examples/example3.jpg"],
    ["fashion_examples/example4.jpg"],
    ["fashion_examples/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.Gallery(label="AI-Generated Outfits (x10)").style(grid=(5, 2), height="auto"),
    ],
    title="👗 Fashion Outfit Recommender",
    description="Upload a clothing image to get 3 similar items from the dataset and 10 AI-generated outfit designs.",
    examples=example_paths
)

if __name__ == "__main__":
    demo.launch()