Upload app.py

app.py

@@ -0,0 +1,145 @@
+# -*- coding: utf-8 -*-
+"""FinalProject.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1_wYfP0IRdb9fpc2zvbg8IqdXGx1dTo7X
+"""
+
+!pip install datasets transformers torch torchvision faiss-cpu gradio
+
+from datasets import load_dataset
+from PIL import Image
+
+# Load dataset from Hugging Face
+dataset = load_dataset("lirus18/deepfashion", split="train")
+
+# Show one image
+from IPython.display import display
+
+image = dataset[0]['image']
+display(image)
+
+from transformers import CLIPProcessor, CLIPModel
+import torch
+
+# Load the CLIP model and processor
+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
+
+image_vectors = []
+image_indices = []
+
+# Use a subset (you can increase to 1000+ later)
+N = 500
+
+for i in range(N):
+    image = dataset[i]['image'].convert("RGB")
+    inputs = processor(images=image, return_tensors="pt").to(device)
+    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_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
+
+    # 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)
+
+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,
+).to(device)
+
+def generate_outfit_from_image(input_image):
+    prompt = "fashion outfit design inspired by the clothing item"
+    init_image = input_image.resize((512, 512))
+    generated = pipe(prompt=prompt, image=init_image, strength=0.7, guidance_scale=7.5)
+    return generated.images[0]
+
+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)):
+        dataset_image = dataset[image_indices[i]]['image'].convert("RGB")
+        if ImageChops.difference(dataset_image, uploaded_image).getbbox() is None:
+            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
+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="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
+)
+
+demo.launch()