Create app.py

app.py

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+import numpy as np
+import pandas as pd
+import gradio as gr
+import torch
+from PIL import Image
+from datasets import load_dataset
+from transformers import CLIPModel, CLIPProcessor
+
+EMB_PATH = "deepfashion_clip_image_embeddings.parquet"
+MODEL_NAME = "openai/clip-vit-base-patch32"
+
+df_emb = pd.read_parquet(EMB_PATH)
+
+required_cols = {"item_ID", "category1", "category2", "embedding"}
+missing = required_cols - set(df_emb.columns)
+if missing:
+    raise ValueError(f"Missing columns in {EMB_PATH}: {missing}")
+
+X = np.stack(df_emb["embedding"].apply(lambda x: np.asarray(x, dtype=np.float32)).to_numpy())
+norms = np.linalg.norm(X, axis=1, keepdims=True)
+norms = np.clip(norms, 1e-12, None)
+Xn = X / norms
+
+meta = df_emb[["item_ID", "category1", "category2"]].copy().reset_index(drop=True)
+
+ds = load_dataset("Marqo/deepfashion-multimodal", split="data")
+id_to_idx = {ds[i]["item_ID"]: i for i in range(len(ds))}
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+clip_model = CLIPModel.from_pretrained(MODEL_NAME).to(device)
+clip_processor = CLIPProcessor.from_pretrained(MODEL_NAME)
+clip_model.eval()
+
+
+def l2_normalize(v: np.ndarray) -> np.ndarray:
+    v = v.astype(np.float32)
+    n = np.linalg.norm(v)
+    if n < 1e-12:
+        return v
+    return v / n
+
+
+@torch.no_grad()
+def embed_text(text: str) -> np.ndarray:
+    inputs = clip_processor(text=[text], return_tensors="pt", padding=True, truncation=True)
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    feats = clip_model.get_text_features(**inputs)
+    vec = feats[0].detach().cpu().numpy().astype(np.float32)
+    return l2_normalize(vec)
+
+
+@torch.no_grad()
+def embed_image_pil(img: Image.Image) -> np.ndarray:
+    inputs = clip_processor(images=img, return_tensors="pt")
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    feats = clip_model.get_image_features(**inputs)
+    vec = feats[0].detach().cpu().numpy().astype(np.float32)
+    return l2_normalize(vec)
+
+
+def topk_recommendations(query_vec: np.ndarray, k: int = 3, exclude_item_id: str | None = None) -> pd.DataFrame:
+    q = l2_normalize(query_vec).astype(np.float32)
+    sims = Xn @ q
+
+    if exclude_item_id is not None:
+        mask = (meta["item_ID"].to_numpy() == exclude_item_id)
+        sims = sims.copy()
+        sims[mask] = -np.inf
+
+    k = min(k, len(sims))
+    idx = np.argpartition(-sims, kth=k - 1)[:k]
+    idx = idx[np.argsort(-sims[idx])]
+
+    out = meta.iloc[idx].copy()
+    out["similarity"] = sims[idx]
+    return out.reset_index(drop=True)
+
+
+def fetch_images(rec_df: pd.DataFrame):
+    gallery = []
+    for _, row in rec_df.iterrows():
+        item_id = row["item_ID"]
+        idx = id_to_idx.get(item_id, None)
+        if idx is None:
+            continue
+        ex = ds[idx]
+        img = ex["image"]
+        caption = f'{row["category1"]}/{row["category2"]}  ({row["similarity"]:.3f})'
+        gallery.append((img, caption))
+    return gallery
+
+
+def recommend_from_text_ui(query: str):
+    if query is None or not query.strip():
+        return pd.DataFrame(columns=["item_ID", "category1", "category2", "similarity"]), []
+    q = embed_text(query.strip())
+    rec = topk_recommendations(q, k=3)
+    return rec, fetch_images(rec)
+
+
+def recommend_from_image_ui(img: Image.Image):
+    if img is None:
+        return pd.DataFrame(columns=["item_ID", "category1", "category2", "similarity"]), []
+    q = embed_image_pil(img)
+    rec = topk_recommendations(q, k=3)
+    return rec, fetch_images(rec)
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# DeepFashion CLIP Recommender (Top-3)")
+
+    with gr.Tab("Text → Top-3"):
+        txt = gr.Textbox(label="Describe an item", placeholder="e.g., a sleeveless summer dress with a floral pattern")
+        btn1 = gr.Button("Recommend")
+        out_table1 = gr.Dataframe(label="Top-3 results", interactive=False)
+        out_gallery1 = gr.Gallery(label="Top-3 images", columns=3, height=320)
+        btn1.click(recommend_from_text_ui, inputs=txt, outputs=[out_table1, out_gallery1])
+
+    with gr.Tab("Image → Top-3"):
+        img_in = gr.Image(type="pil", label="Upload an image")
+        btn2 = gr.Button("Recommend")
+        out_table2 = gr.Dataframe(label="Top-3 results", interactive=False)
+        out_gallery2 = gr.Gallery(label="Top-3 images", columns=3, height=320)
+        btn2.click(recommend_from_image_ui, inputs=img_in, outputs=[out_table2, out_gallery2])
+
+demo.launch()