Delete app.py

app.py

@@ -1,162 +0,0 @@
-import gradio as gr
-import torch
-from sentence_transformers import SentenceTransformer
-from ddgs import DDGS
-
-# Load Model
-model = SentenceTransformer(
-    "RikkaBotan/stable-static-embedding-fast-retrieval-mrl-en",
-    trust_remote_code=True,
-    device="cuda" if torch.cuda.is_available() else "cpu"
-)
-
-# Web Search
-def web_search(query, max_results=100):
-    results = []
-    with DDGS() as ddgs:
-        for r in ddgs.text(query, max_results=max_results):
-            results.append({
-                "title": r.get("title", ""),
-                "body": r.get("body", ""),
-                "href": r.get("href", "")
-            })
-    return results
-
-# Semantic Ranking
-def semantic_web_search(query):
-    if query.strip() == "":
-        return "Please enter a search query."
-
-    docs = web_search(query, max_results=100)
-    texts = [d["title"] + " " + d["body"] for d in docs]
-
-    with torch.no_grad():
-        embeddings = model.encode(
-            [query] + texts[:256],
-            convert_to_tensor=True,
-            normalize_embeddings=True
-        )
-
-    query_emb = embeddings[0]
-    doc_embs = embeddings[1:]
-    scores = (query_emb @ doc_embs.T).cpu().numpy()
-
-    ranked = sorted(zip(scores, docs), key=lambda x: x[0], reverse=True)[:30]
-
-    md = ""
-    for i, (score, d) in enumerate(ranked):
-        md += f"""
-### 💎 Rank {i+1}
-**[{d['title']}]({d['href']})**
-
-**Similarity Score:** `{score:.4f}`
-
-{d['body']}
-
----
-"""
-    return md
-
-
-pastel_css = """
-body {
-    background: linear-gradient(180deg, #f4f9ff 0%, #eaf4ff 40%, #f9fcff 100%);
-}
-
-.gradio-container {
-    font-family: 'Helvetica Neue', sans-serif;
-    color: #4a6fa5;
-}
-
-/* model card */
-.model-card {
-    background: #ffffff;
-    border-radius: 18px;
-    padding: 22px;
-    border: 1px solid #e3efff;
-    box-shadow: 0 12px 18px rgba(100, 150, 255, 0.15);
-    margin-bottom: 20px;
-}
-
-/* result card */
-.result-card {
-    background: #ffffff;
-    border-radius: 18px;
-    padding: 22px;
-    border: 1px solid #e3efff;
-    box-shadow: 0 12px 18px rgba(100, 150, 255, 0.15);
-}
-
-/* remove markdown bg */
-.gr-markdown, .prose {
-    background: transparent !important;
-    border: none !important;
-    box-shadow: none !important;
-    padding: 0 !important;
-}
-
-/* input */
-textarea, input {
-    border-radius: 12px !important;
-    border: 1px solid #dbe9ff !important;
-    background-color: #f8fbff !important;
-}
-
-/* button */
-button {
-    background: linear-gradient(90deg, #a8d0ff, #bde0ff) !important;
-    color: white !important;
-    border-radius: 14px !important;
-    border: none !important;
-    font-weight: 600;
-    box-shadow: 0 4px 10px rgba(120,170,255,0.25);
-}
-
-button:hover {
-    background: linear-gradient(90deg, #91c4ff, #a9d6ff) !important;
-}
-"""
-
-
-# UI
-with gr.Blocks(css=pastel_css) as demo:
-
-    gr.Markdown('# Semantic Web Search')
-    gr.Markdown('## Fast Semantic Retrieval with Stable Static Embedding')
-
-    with gr.Column(elem_classes="model-card"):
-        gr.Markdown("""
-## About this Model
-
-**RikkaBotan/stable-static-embedding-fast-retrieval-mrl-en**
-
-### Performance
-- NanoBEIR NDCG@10 = **0.5124**
-- Higher than static-retrieval-mrl-en-v1 (0.5032)
-
-### Efficiency
-- 512 dimensions (half size)
-- ~2× faster retrieval
-- Separable Dynamic Tanh normalization
-""")
-
-    query = gr.Textbox(
-        value="What is Stable Static Embedding?",
-        label="Enter your search query",
-        placeholder="Example: What is Stable Static Embedding?"
-    )
-
-    search_btn = gr.Button("Search")
-
-    with gr.Column(elem_classes="result-card"):
-        results = gr.Markdown()
-
-    search_btn.click(
-        semantic_web_search,
-        inputs=query,
-        outputs=results
-    )
-
-    gr.Markdown("© 2026 Rikka Botan")
-
-demo.launch()