Create app.py

app.py

@@ -0,0 +1,50 @@
+import pandas as pd
+from sentence_transformers import SentenceTransformer, util
+import gradio as gr
+
+# ─── Load catalog & compute embeddings ─────────────────────────
+PRODUCTS_CSV = "products.csv"
+df = pd.read_csv(PRODUCTS_CSV)
+descriptions = df["description"].tolist()
+
+model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+embeddings = model.encode(descriptions, convert_to_tensor=True, normalize_embeddings=True)
+
+# ─── Semantic-search function ─────────────────────────────────
+def search_products(query: str, top_k: int = 5):
+    if not query.strip():
+        return pd.DataFrame(columns=df.columns.tolist() + ["score"])
+    q_emb = model.encode(query, convert_to_tensor=True, normalize_embeddings=True)
+    hits = util.cos_sim(q_emb, embeddings)[0].topk(k=top_k)
+    indices = hits.indices.cpu().tolist()
+    scores = [round(float(s), 3) for s in hits.values.cpu().tolist()]
+
+    results = df.iloc[indices].copy()
+    results["score"] = scores
+    return results.reset_index(drop=True)
+
+# ─── Gradio UI ────────────────────────────────────────────────
+with gr.Blocks(title="🛍️ Salon Catalog Semantic Search") as demo:
+    gr.Markdown("""
+    # 🛍️ Salon Product Search  
+    **Natural-language queries** → **top matching products** via MiniLM embeddings  
+    (Runs entirely on free CPU; no paid APIs)
+    """)
+
+    with gr.Row():
+        query_in = gr.Textbox(placeholder="e.g. sulfate-free shampoo under $15", label="Search query")
+        topk     = gr.Slider(1, 10, value=5, step=1, label="Number of results")
+    search_btn = gr.Button("Search 🔍", variant="primary")
+
+    table = gr.Dataframe(
+        headers=list(df.columns) + ["score"],
+        datatype=["number","str","str","str","number","number"],
+        interactive=False,
+        row_count= topk.value,
+        label="Results"
+    )
+
+    search_btn.click(search_products, [query_in, topk], table)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0")