Delete app.py

app.py

@@ -1,99 +0,0 @@
-import json
-from typing import Any, Dict
-
-import numpy as np
-import tensorflow as tf
-from fastapi import FastAPI, HTTPException
-
-# -----------------
-# Config
-# -----------------
-MODEL_PATH = "best_model.h5"      # upload alongside app.py
-STATS_PATH = "means_std.json"     # {"feature":{"mean":..., "std":...}, ...}
-CLASSES = ["Top", "Mid-Top", "Mid", "Mid-Low", "Low"]
-
-# -----------------
-# Load artifacts
-# -----------------
-print("Loading model and stats...")
-model = tf.keras.models.load_model(MODEL_PATH, compile=False)
-
-with open(STATS_PATH, "r") as f:
-    stats: Dict[str, Dict[str, float]] = json.load(f)
-
-FEATURES = list(stats.keys())
-print("Feature order:", FEATURES)
-
-# -----------------
-# Helpers
-# -----------------
-def _z(v: Any, mean: float, sd: float) -> float:
-    try:
-        x = float(v)
-    except Exception:
-        return 0.0
-    if not sd or sd == 0.0:
-        return 0.0
-    return (x - mean) / sd
-
-def _coral_probs_from_logits(logits_np: np.ndarray) -> np.ndarray:
-    """
-    For ordinal (K-1 logits): p_k = σ(z_{k-1}) - σ(z_k) with σ(z_0)=1, σ(z_K)=0
-    """
-    logits = tf.convert_to_tensor(logits_np, dtype=tf.float32)
-    sig = tf.math.sigmoid(logits)                  # (N, K-1)
-    left  = tf.concat([tf.ones_like(sig[:, :1]), sig], axis=1)
-    right = tf.concat([sig, tf.zeros_like(sig[:, :1])], axis=1)
-    probs = tf.clip_by_value(left - right, 1e-12, 1.0)
-    return probs.numpy()
-
-def _predict_core(ratios: Dict[str, Any]) -> Dict[str, Any]:
-    missing = [f for f in FEATURES if f not in ratios]
-
-    z = [ _z(ratios.get(f, 0.0), stats[f]["mean"], stats[f]["std"]) for f in FEATURES ]
-    X = np.array([z], dtype=np.float32)  # (1, D)
-
-    raw = model.predict(X, verbose=0)    # (1, K) or (1, K-1)
-    if raw.ndim != 2:
-        raise ValueError(f"Unexpected model output shape: {raw.shape}")
-
-    if raw.shape[1] == len(CLASSES) - 1:
-        probs = _coral_probs_from_logits(raw)[0]
-    elif raw.shape[1] == len(CLASSES):
-        probs = raw[0]
-    else:
-        raise ValueError(f"Model output width {raw.shape[1]} incompatible with {len(CLASSES)} classes")
-
-    probs = np.maximum(probs, 0.0)
-    s = probs.sum()
-    probs = probs / s if s > 0 else np.ones(len(CLASSES)) / len(CLASSES)
-
-    pred_idx = int(np.argmax(probs))
-    return {
-        "input_ok": len(missing) == 0,
-        "missing": missing,
-        "probabilities": {CLASSES[i]: float(probs[i]) for i in range(len(CLASSES))},
-        "predicted_state": CLASSES[pred_idx],
-    }
-
-# -----------------
-# FastAPI app
-# -----------------
-app = FastAPI()
-
-@app.get("/health")
-def health():
-    return {"status": "ok", "features": FEATURES, "classes": CLASSES}
-
-@app.post("/predict")
-def predict(payload: Any):
-    # Accept either {feature:value,...} or [ {feature:value,...} ]
-    if isinstance(payload, list) and len(payload) == 1 and isinstance(payload[0], dict):
-        payload = payload[0]
-    if not isinstance(payload, dict):
-        raise HTTPException(status_code=400, detail="Expected JSON object mapping feature -> value.")
-
-    try:
-        return _predict_core(payload)
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))