Delete app.py

app.py

@@ -1,297 +0,0 @@
-# ============================================================
-# ⚡ Ultra-Fast Semantic Intent Router
-# MiniLM-based, zero-training, sub-second classification
-# Works on Hugging Face Free CPU
-# ============================================================
-
-import json
-import math
-import time
-from typing import Dict, List, Tuple, Any
-
-import torch
-import gradio as gr
-from sentence_transformers import SentenceTransformer, util
-
-# ============================================================
-# 🔧 CONFIG
-# ============================================================
-
-MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
-MAX_EXAMPLES_PER_LABEL = 20
-NORMALIZE_SCORES = True
-MIN_CONFIDENCE = 0.05
-
-# ============================================================
-# 🧠 MODEL LOAD
-# ============================================================
-
-print("🔄 Loading MiniLM model...")
-model = SentenceTransformer(MODEL_NAME, device="cpu")
-print("✅ Model loaded")
-
-# ============================================================
-# 🧩 UTILITIES
-# ============================================================
-
-def cosine_similarity(a: torch.Tensor, b: torch.Tensor) -> float:
-    return util.cos_sim(a, b).item()
-
-
-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())
-
-    if total == 0:
-        return {k: 0.0 for k in scores}
-
-    return {k: v / total for k, v in exp_scores.items()}
-
-
-# ============================================================
-# 🧱 LABEL PARSING (CRITICAL FIX)
-# ============================================================
-
-def parse_labels(raw: Any) -> Dict[str, List[str]]:
-    """
-    Handles:
-    - dict (Gradio JSON component)
-    - stringified JSON
-    - rejects invalid structures safely
-    """
-
-    if isinstance(raw, dict):
-        labels = raw
-
-    elif isinstance(raw, str):
-        try:
-            labels = json.loads(raw)
-        except Exception as e:
-            return {"__error__": f"Invalid JSON: {e}"}
-
-    else:
-        return {"__error__": "Labels must be JSON object"}
-
-    if not isinstance(labels, dict):
-        return {"__error__": "Top-level JSON must be object"}
-
-    clean = {}
-
-    for label, examples in labels.items():
-        if not isinstance(label, str):
-            continue
-
-        if not isinstance(examples, list):
-            continue
-
-        cleaned_examples = [
-            str(x).strip()
-            for x in examples
-            if isinstance(x, (str, int, float)) and str(x).strip()
-        ][:MAX_EXAMPLES_PER_LABEL]
-
-        if cleaned_examples:
-            clean[label] = cleaned_examples
-
-    if not clean:
-        return {"__error__": "No valid labels found"}
-
-    return clean
-
-
-# ============================================================
-# 🧠 ROUTER CORE
-# ============================================================
-
-def classify_intent(
-    text: str,
-    labels: Dict[str, List[str]],
-) -> Dict[str, Any]:
-    start = time.time()
-
-    if not text or not text.strip():
-        return {"error": "Input text is empty"}
-
-    text = text.strip()
-
-    # Encode input
-    input_emb = model.encode(text, convert_to_tensor=True)
-
-    label_scores: Dict[str, float] = {}
-
-    for label, examples in labels.items():
-        example_embs = model.encode(examples, convert_to_tensor=True)
-
-        sims = util.cos_sim(input_emb, example_embs)[0]
-        score = float(torch.max(sims).item())
-
-        if score >= MIN_CONFIDENCE:
-            label_scores[label] = score
-
-    if not label_scores:
-        return {
-            "text": text,
-            "top_intent": None,
-            "scores": {},
-            "latency_ms": round((time.time() - start) * 1000, 2),
-        }
-
-    if NORMALIZE_SCORES:
-        label_scores = softmax(label_scores)
-
-    top_intent = max(label_scores, key=label_scores.get)
-
-    return {
-        "text": text,
-        "top_intent": top_intent,
-        "scores": dict(sorted(label_scores.items(), key=lambda x: -x[1])),
-        "latency_ms": round((time.time() - start) * 1000, 2),
-    }
-
-
-# ============================================================
-# 🌐 API WRAPPER (Gradio-compatible)
-# ============================================================
-
-def api_route(text: str, raw_labels: Any) -> Dict[str, Any]:
-    labels = parse_labels(raw_labels)
-
-    if "__error__" in labels:
-        return {"error": labels["__error__"]}
-
-    return classify_intent(text, labels)
-
-
-# ============================================================
-# 🖥️ DEFAULT LABEL SET
-# ============================================================
-
-DEFAULT_LABELS = {
-    "chat": [
-        "say hello",
-        "casual conversation",
-        "how are you",
-    ],
-    "search": [
-        "find information",
-        "look this up",
-        "search online",
-    ],
-    "image_generation": [
-        "generate an image",
-        "draw a picture",
-        "create artwork",
-    ],
-    "code": [
-        "write code",
-        "debug this program",
-        "fix this bug",
-    ],
-    "research": [
-        "deep analysis",
-        "investigate topic",
-        "academic research",
-    ],
-    "study": [
-        "teach me",
-        "explain concept",
-        "help me learn",
-    ],
-    "project": [
-        "build a project",
-        "start a new app",
-        "create software",
-    ],
-    "action": [
-        "set a timer",
-        "create reminder",
-        "schedule event",
-    ],
-}
-
-
-# ============================================================
-# 🎛️ GRADIO UI
-# ============================================================
-
-with gr.Blocks(title="⚡ Semantic Intent Router") as demo:
-    gr.Markdown(
-        """
-# ⚡ Semantic Intent Router Builder
-**MiniLM semantic classifier**  
-No training · CPU-only · Sub-second · API ready
-
-• Edit labels & examples freely  
-• Add unlimited labels  
-• Used for system-prompt injection & MPC routing  
-"""
-    )
-
-    with gr.Row():
-        user_input = gr.Textbox(
-            label="User Input",
-            placeholder="Type anything...",
-            lines=2,
-        )
-
-    with gr.Row():
-        labels_json = gr.JSON(
-            label="Labels & Examples (edit freely, add/remove)",
-            value=DEFAULT_LABELS,
-        )
-
-    with gr.Row():
-        output = gr.JSON(label="Routing Result")
-
-    classify_btn = gr.Button("Classify", variant="primary")
-
-    classify_btn.click(
-        fn=api_route,
-        inputs=[user_input, labels_json],
-        outputs=output,
-    )
-
-    gr.Markdown(
-        """
-### 🔌 API Usage
-
-**POST** to this Space URL:
-
-```json
-{
-  "data": [
-    "Set a timer for 10 minutes",
-    {
-      "chat": ["hello", "hi"],
-      "action": ["set a timer", "remind me"]
-    }
-  ]
-}
-
-Response:
-
-{
-  "top_intent": "action",
-  "scores": { ... }
-}
-
-“””
-)
-
-============================================================
-
-🚀 LAUNCH
-
-============================================================
-
-if name == “main”:
-demo.launch(
-share=True,
-enable_queue=False,
-)
-
----