Delete finalproject.py

finalproject.py

@@ -1,145 +0,0 @@
-# -*- 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()