Add application file

app.py

@@ -0,0 +1,49 @@
+import re
+import joblib
+import gradio as gr
+from sentence_transformers import SentenceTransformer
+
+enc = SentenceTransformer("all-MiniLM-L6-v2")
+clf = joblib.load("model.pkl")
+meta = joblib.load("meta.pkl")
+threshold = meta["threshold"]
+
+EXAMPLES = [
+    ["What is 2 + 2?"],
+    ["Write a Python function to reverse a linked list."],
+    ["Explain how transformers handle long-range dependencies in NLP."],
+    ["What microscopic mechanisms reconcile correlated insulating phases with unconventional superconductivity in magic-angle twisted bilayer graphene?"],
+]
+
+def predict(q):
+    if not q.strip():
+        return "—", "—"
+    emb = enc.encode([q])
+    prob = clf.predict_proba(emb)[0][1]  # prob of "complex"
+    label = "🔴 Complex" if prob > 0.5 else "🟢 Simple"
+    confidence = f"{max(prob, 1-prob):.1%}"
+    return label, confidence
+
+with gr.Blocks(title="Question Complexity Classifier") as demo:
+    gr.Markdown(
+        """
+# 🧠 Question Complexity Classifier
+Predicts whether a question requires **long chain-of-thought reasoning** or not.  
+Trained on 20k samples from [KIMI-K2.5-700000x](https://huggingface.co/datasets/ianncity/KIMI-K2.5-700000x) reasoning traces.  
+Complexity proxy: CoT length > {:.0f} chars = Complex.
+        """.format(threshold)
+    )
+
+    with gr.Row():
+        inp = gr.Textbox(label="Question", lines=4, placeholder="Enter any question...")
+        with gr.Column():
+            out_label = gr.Textbox(label="Complexity")
+            out_conf = gr.Textbox(label="Confidence")
+
+    btn = gr.Button("Predict", variant="primary")
+    btn.click(predict, inputs=inp, outputs=[out_label, out_conf])
+    inp.submit(predict, inputs=inp, outputs=[out_label, out_conf])
+
+    gr.Examples(examples=EXAMPLES, inputs=inp, label="Try these")
+
+demo.launch()