Create app.py

app.py

@@ -0,0 +1,92 @@
+# app.py
+import streamlit as st
+import joblib, json, numpy as np
+from pathlib import Path
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+
+ART = Path("artifacts")
+LABELS = json.load(open(ART/"label_names.json"))
+EMB_MODEL_NAME = (ART/"emb_model_name.txt").read_text().strip()
+
+@st.cache_resource(show_spinner=False)
+def load_models():
+    emb = SentenceTransformer(EMB_MODEL_NAME)
+    clf = joblib.load(ART/"lgbm_model.pkl")
+    nn = joblib.load(ART/"nn_index.pkl")
+    tfidf = joblib.load(ART/"tfidf_explainer.pkl")
+    train_meta = json.load(open(ART/"train_meta.json"))
+    class_keywords = json.load(open(ART/"class_keywords.json"))
+    return emb, clf, nn, tfidf, train_meta, class_keywords
+
+def encode_one(emb_model, text: str) -> np.ndarray:
+    text = text.strip()
+    prompt = f"passage: {text}"
+    v = emb_model.encode([prompt], show_progress_bar=False, normalize_embeddings=True)
+    return np.asarray(v, dtype=np.float32)
+
+st.set_page_config(page_title="ArXiv Abstract Classifier", page_icon="🧠", layout="wide")
+st.title("🧠 ArXiv Abstract Classifier")
+st.caption("Embeddings (E5) + LightGBM • Probabilities • Similar Papers • Class Keywords")
+
+with st.sidebar:
+    st.markdown("### Settings")
+    topk = st.slider("Top similar papers", 1, 10, 3)
+    show_keywords = st.checkbox("Show class keywords", value=True)
+    st.divider()
+    st.markdown("Model")
+    st.code(f"Encoder: {EMB_MODEL_NAME}\nClassifier: LightGBM", language="yaml")
+
+emb_model, clf, nn, tfidf, train_meta, class_keywords = load_models()
+
+default_text = """We propose a novel neural architecture for efficient transformer inference,
+reducing memory footprint while maintaining accuracy on common NLP tasks. 
+Experiments on translation and summarization demonstrate competitive results."""
+text = st.text_area("Paste paper abstract here:", default_text, height=220)
+
+col1, col2 = st.columns([1,1])
+with col1:
+    run = st.button("🔍 Classify")
+with col2:
+    clear = st.button("🧹 Clear")
+    if clear:
+        st.experimental_rerun()
+
+if run:
+    if not text.strip():
+        st.warning("Please enter an abstract.")
+        st.stop()
+
+    v = encode_one(emb_model, text)
+    probs = clf.predict_proba(v)[0]
+    pred_idx = int(np.argmax(probs))
+    pred_label = LABELS[pred_idx]
+
+    st.success(f"**Predicted field:** `{pred_label}`")
+    st.write("### Class probabilities")
+    prob_dict = {LABELS[i]: float(probs[i]) for i in range(len(LABELS))}
+    st.bar_chart(prob_dict)
+
+    st.write("### 🔗 Most similar training papers")
+    dists, idxs = nn.kneighbors(v, n_neighbors=max(topk, 3), return_distance=True)
+    idxs = idxs[0].tolist()
+    dists = dists[0].tolist()
+
+    titles = train_meta["train_titles"]
+    abstracts = train_meta["train_abstracts"]
+    labels = train_meta["train_labels"]
+
+    for rank, (i, d) in enumerate(zip(idxs[:topk], dists[:topk]), start=1):
+        cos = 1 - d
+        with st.container(border=True):
+            st.markdown(f"**#{rank}. {titles[i]}**")
+            st.caption(f"_Label:_ `{labels[i]}` • _Cosine similarity:_ **{cos:.3f}**")
+            st.write(abstracts[i][:600] + ("..." if len(abstracts[i]) > 600 else ""))
+
+    if show_keywords:
+        st.write("### 🏷️ Class keywords (TF-IDF centroids)")
+        cols = st.columns(len(LABELS))
+        for j, lb in enumerate(LABELS):
+            with cols[j]:
+                st.markdown(f"**{lb}**")
+                st.write(", ".join(class_keywords.get(lb, [])[:15]))