Create app.py

app.py

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+import gradio as gr
+import torch
+from sentence_transformers import SentenceTransformer, util
+
+# -----------------------------
+# Load model (FAST)
+# -----------------------------
+model = SentenceTransformer("all-MiniLM-L6-v2")
+
+# -----------------------------
+# Intent anchors
+# -----------------------------
+INTENT_LABELS = {
+    "chat": [
+        "casual conversation",
+        "small talk",
+        "talk to me",
+        "just chatting"
+    ],
+    "search": [
+        "find information",
+        "look up facts",
+        "search the web"
+    ],
+    "image_generation": [
+        "generate an image",
+        "create artwork",
+        "draw a picture"
+    ],
+    "code": [
+        "write code",
+        "debug program",
+        "programming help"
+    ],
+    "research": [
+        "deep research",
+        "academic analysis",
+        "investigate topic"
+    ],
+    "study": [
+        "help me learn",
+        "teach me something",
+        "study help"
+    ],
+    "project": [
+        "build a project",
+        "create an application",
+        "long term development"
+    ],
+    "action": [
+        "set a reminder",
+        "schedule something",
+        "perform an action"
+    ]
+}
+
+# -----------------------------
+# Precompute anchor embeddings
+# -----------------------------
+anchor_embeddings = {
+    intent: model.encode(phrases, convert_to_tensor=True)
+    for intent, phrases in INTENT_LABELS.items()
+}
+
+# -----------------------------
+# Classifier
+# -----------------------------
+def classify(text: str):
+    if not text.strip():
+        return {"error": "Empty input"}
+
+    query_emb = model.encode(text, convert_to_tensor=True)
+
+    scores = {}
+    for intent, emb in anchor_embeddings.items():
+        score = util.cos_sim(query_emb, emb).max().item()
+        scores[intent] = score
+
+    top_intent = max(scores, key=scores.get)
+
+    return {
+        "text": text,
+        "top_intent": top_intent,
+        "scores": scores
+    }
+
+# -----------------------------
+# Gradio UI
+# -----------------------------
+demo = gr.Interface(
+    fn=classify,
+    inputs=gr.Textbox(label="User Input"),
+    outputs=gr.JSON(),
+    title="⚡ Ultra-Fast Intent Router",
+    description="Sub-second semantic routing using MiniLM (no training)"
+)
+
+app = demo