Delete app.py

app.py

@@ -1,175 +0,0 @@
-import os
-import pickle
-import numpy as np
-import gradio as gr
-import torch
-from datasets import load_dataset
-from PIL import Image
-from transformers import AutoImageProcessor, AutoModel
-from diffusers import StableDiffusionPipeline
-
-# ==========================================================
-# CONFIG
-# ==========================================================
-DATASET_ID = "MAY199/synthetic-sofa-images"
-PICKLE_PATH = "sofa_embeddings_for_app.pkl"
-
-AI_PROMPT = (
-    "a modern minimalist sofa, neutral colors, scandinavian interior style, "
-    "high quality product photo, soft natural light, sharp focus"
-)
-NEGATIVE_PROMPT = (
-    "low quality, blurry, distorted, extra legs, extra cushions, watermark, text, logo, "
-    "deformed, bad proportions, cartoon, painting, sketch"
-)
-SD_MODEL_ID = "runwayml/stable-diffusion-v1-5"
-
-# ==========================================================
-# LOAD DATASET (HF)
-# ==========================================================
-ds = load_dataset(DATASET_ID)
-train_ds = ds["train"]
-
-# ==========================================================
-# LOAD EMBEDDINGS (ViT)
-# ==========================================================
-with open(PICKLE_PATH, "rb") as f:
-    data = pickle.load(f)
-
-model_id = data["model_id"]
-emb_matrix = data["embeddings"].astype(np.float32)
-image_indices = data["image_indices"]
-
-if "vit" not in model_id.lower():
-    raise ValueError(f"This app expects a ViT model_id, got: {model_id}")
-
-# ==========================================================
-# LOAD VIT
-# ==========================================================
-device = "cuda" if torch.cuda.is_available() else "cpu"
-processor = AutoImageProcessor.from_pretrained(model_id, use_fast=True)
-model = AutoModel.from_pretrained(model_id).to(device)
-model.eval()
-
-def l2_normalize(x):
-    return x / (np.linalg.norm(x) + 1e-12)
-
-@torch.no_grad()
-def embed_image(img: Image.Image) -> np.ndarray:
-    inputs = processor(images=img, return_tensors="pt")
-    inputs = {k: v.to(device) for k, v in inputs.items()}
-    outputs = model(**inputs)
-    feats = outputs.last_hidden_state.mean(dim=1)
-    feats = feats / feats.norm(dim=-1, keepdim=True)
-    return feats.squeeze(0).float().cpu().numpy()
-
-# ==========================================================
-# LOAD SD (lazy-load to avoid blocking startup)
-# ==========================================================
-pipe = None
-
-def get_pipe():
-    global pipe
-    if pipe is not None:
-        return pipe
-
-    # print device so you can see in Logs if it's CPU!
-    print("Stable Diffusion device:", device)
-
-    sd_dtype = torch.float16 if device == "cuda" else torch.float32
-    p = StableDiffusionPipeline.from_pretrained(
-        SD_MODEL_ID,
-        torch_dtype=sd_dtype,
-        safety_checker=None,
-    ).to(device)
-
-    if device == "cuda":
-        try:
-            p.enable_attention_slicing()
-        except Exception:
-            pass
-
-    pipe = p
-    return pipe
-
-def generate_ai_recommendation():
-    p = get_pipe()
-    with torch.no_grad():
-        img = p(
-            prompt=AI_PROMPT,
-            negative_prompt=NEGATIVE_PROMPT,
-            num_inference_steps=12,   # ↓ faster
-            guidance_scale=6.0
-        ).images[0]
-    return img
-
-# ==========================================================
-# 1) FAST RETRIEVAL (Submit)
-# ==========================================================
-def recommend_fast(img: Image.Image):
-    q = l2_normalize(embed_image(img)).astype(np.float32)
-    sims = emb_matrix @ q
-    top_idx = np.argsort(-sims)
-
-    # debug
-    print("Top 5 sims:", sims[top_idx[:5]])
-
-    results = []
-    for j in top_idx:
-        if sims[j] > 0.999:
-            continue
-        ds_idx = image_indices[j]
-        results.append(train_ds[int(ds_idx)]["image"])
-        if len(results) == 3:
-            break
-
-    while len(results) < 3:
-        ds_idx = image_indices[int(top_idx[len(results)])]
-        results.append(train_ds[int(ds_idx)]["image"])
-
-    return results[0], results[1], results[2]
-
-# ==========================================================
-# 2) SLOW GENERATION (button)
-# ==========================================================
-def recommend_ai():
-    return generate_ai_recommendation()
-
-# ==========================================================
-# UI
-# ==========================================================
-with gr.Blocks() as app:
-    gr.Markdown("# Sofa Recommendation System + AI Generation")
-    gr.Markdown("Submit returns 3 similar sofas quickly. Generate AI creates a new sofa image (can be slow).")
-
-    inp = gr.Image(type="pil", label="Upload a sofa image")
-
-    # ======================================================
-    # QUICK STARTERS (NEW)
-    # ======================================================
-    gr.Markdown("## Quick Starters (1-click examples)")
-    gr.Markdown("Click an example image to auto-fill the input, then press **Submit (Fast)**.")
-
-    gr.Examples(
-        examples=[
-            "examples/starter1.jpeg",
-            "examples/starter2.jpeg",
-            "examples/starter3.jpeg",
-        ],
-        inputs=inp,
-        label="Quick Starters",
-    )
-
-    with gr.Row():
-        btn_submit = gr.Button("Submit (Fast)")
-        btn_ai = gr.Button("Generate AI Recommendation (Slow)")
-
-    out1 = gr.Image(label="Recommendation 1")
-    out2 = gr.Image(label="Recommendation 2")
-    out3 = gr.Image(label="Recommendation 3")
-    out_ai = gr.Image(label="AI Recommendation")
-
-    btn_submit.click(fn=recommend_fast, inputs=inp, outputs=[out1, out2, out3])
-    btn_ai.click(fn=recommend_ai, inputs=None, outputs=out_ai)
-
-app.launch()