Create app.py

app.py

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+# ============================================================
+# ⚡ Semantic Intent Router (MiniLM)
+# Zero-shot • No training • Sub-second • HF Free CPU
+# ============================================================
+
+import json
+import time
+import math
+from typing import Dict, List, Any
+
+import torch
+import gradio as gr
+from sentence_transformers import SentenceTransformer, util
+
+# ============================================================
+# CONFIG
+# ============================================================
+
+MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
+MIN_SCORE = 0.05
+MAX_EXAMPLES = 20
+
+# ============================================================
+# LOAD MODEL
+# ============================================================
+
+print("Loading MiniLM model...")
+model = SentenceTransformer(MODEL_NAME, device="cpu")
+print("Model loaded")
+
+# ============================================================
+# HELPERS
+# ============================================================
+
+def softmax(scores: Dict[str, float]) -> Dict[str, float]:
+    if not scores:
+        return {}
+
+    max_val = max(scores.values())
+    exp_scores = {k: math.exp(v - max_val) for k, v in scores.items()}
+    total = sum(exp_scores.values())
+
+    return {k: v / total for k, v in exp_scores.items()}
+
+
+def parse_labels(raw: Any) -> Dict[str, List[str]]:
+    """
+    Accepts dict (Gradio JSON) or JSON string.
+    Returns clean label -> examples mapping.
+    """
+
+    if isinstance(raw, str):
+        try:
+            raw = json.loads(raw)
+        except Exception as e:
+            return {"__error__": f"Invalid JSON: {e}"}
+
+    if not isinstance(raw, dict):
+        return {"__error__": "Labels must be a JSON object"}
+
+    cleaned = {}
+
+    for label, examples in raw.items():
+        if not isinstance(label, str):
+            continue
+        if not isinstance(examples, list):
+            continue
+
+        ex = [
+            str(x).strip()
+            for x in examples
+            if isinstance(x, (str, int, float)) and str(x).strip()
+        ][:MAX_EXAMPLES]
+
+        if ex:
+            cleaned[label] = ex
+
+    if not cleaned:
+        return {"__error__": "No valid labels found"}
+
+    return cleaned
+
+
+# ============================================================
+# CLASSIFIER CORE
+# ============================================================
+
+def classify(text: str, raw_labels: Any) -> Dict[str, Any]:
+    start = time.time()
+
+    if not text or not text.strip():
+        return {"error": "Empty input"}
+
+    labels = parse_labels(raw_labels)
+    if "__error__" in labels:
+        return {"error": labels["__error__"]}
+
+    text_emb = model.encode(text, convert_to_tensor=True)
+
+    scores = {}
+
+    for label, examples in labels.items():
+        example_embs = model.encode(examples, convert_to_tensor=True)
+        sims = util.cos_sim(text_emb, example_embs)[0]
+        score = float(torch.max(sims).item())
+
+        if score >= MIN_SCORE:
+            scores[label] = score
+
+    if not scores:
+        return {
+            "text": text,
+            "top_intent": None,
+            "scores": {},
+            "latency_ms": round((time.time() - start) * 1000, 2),
+        }
+
+    scores = softmax(scores)
+    top_intent = max(scores, key=scores.get)
+
+    return {
+        "text": text,
+        "top_intent": top_intent,
+        "scores": dict(sorted(scores.items(), key=lambda x: -x[1])),
+        "latency_ms": round((time.time() - start) * 1000, 2),
+    }
+
+
+# ============================================================
+# DEFAULT LABELS
+# ============================================================
+
+DEFAULT_LABELS = {
+    "chat": [
+        "say hello",
+        "casual talk",
+        "how are you"
+    ],
+    "image_generation": [
+        "generate an image",
+        "draw a picture",
+        "create artwork"
+    ],
+    "action": [
+        "set a timer",
+        "create a reminder"
+    ],
+    "code": [
+        "write code",
+        "debug program"
+    ],
+    "search": [
+        "search online",
+        "find information"
+    ]
+}
+
+# ============================================================
+# GRADIO UI
+# ============================================================
+
+with gr.Blocks(title="⚡ Semantic Intent Router") as demo:
+    gr.Markdown(
+        "# ⚡ Semantic Intent Router\n"
+        "MiniLM semantic classifier · No training · Sub-second\n\n"
+        "• Edit labels freely\n"
+        "• Add examples per label\n"
+        "• Used for MPC / system-prompt routing\n"
+    )
+
+    user_input = gr.Textbox(
+        label="User Input",
+        placeholder="Type anything…",
+        lines=2
+    )
+
+    labels_input = gr.JSON(
+        label="Labels & Examples (editable)",
+        value=DEFAULT_LABELS
+    )
+
+    output = gr.JSON(label="Routing Result")
+
+    classify_btn = gr.Button("Classify", variant="primary")
+
+    classify_btn.click(
+        fn=classify,
+        inputs=[user_input, labels_input],
+        outputs=output
+    )
+
+    gr.Markdown(
+        "### API Usage\n"
+        "POST to this Space endpoint with:\n\n"
+        "`{\"data\": [\"your text\", {\"label\": [\"example\"]}]}`\n"
+    )
+
+# ============================================================
+# LAUNCH
+# ============================================================
+
+if __name__ == "__main__":
+    demo.launch(
+        share=True,
+        enable_queue=False
+    )