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import os
import json
import numpy as np
import tensorflow as tf
import gradio as gr
# ---------- CONFIG ----------
MODEL_PATH = "best_model.h5" # or best_model.keras
STATS_PATH = "Means & Std for Excel.json" # must match filename in repo
CLASSES = ["Top", "Mid-Top", "Mid", "Mid-Low", "Low"]
# ----------------------------
print("Loading model and stats...")
model = tf.keras.models.load_model(MODEL_PATH, compile=False)
with open(STATS_PATH, "r") as f:
stats = json.load(f)
FEATURES = list(stats.keys())
print("Feature order:", FEATURES)
# ---------- Utility helpers ----------
def _zscore(val: float, mean: float, sd: float) -> float:
"""Compute safe z-score (handles NaNs and zeros)."""
try:
v = float(val)
except (TypeError, ValueError):
v = 0.0
return 0.0 if (sd is None or sd == 0) else (v - mean) / sd
def coral_probs_from_logits(logits_np):
"""Convert (N, K-1) CORAL logits → (N, K) probabilities."""
logits = tf.convert_to_tensor(logits_np, dtype=tf.float32)
sig = tf.math.sigmoid(logits)
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):
"""
ratios: dict mapping feature name -> raw numeric ratio.
Returns dict with predicted_state, probabilities, z_scores, missing.
"""
missing = [f for f in FEATURES if f not in ratios]
# Build z-score vector in same feature order
zscores, zscores_dict = [], {}
for f in FEATURES:
mean = stats[f]["mean"]
sd = stats[f]["std"]
val = ratios.get(f, 0.0)
z = _zscore(val, mean, sd)
zscores.append(z)
zscores_dict[f] = z
X = np.array([zscores], dtype=np.float32)
logits = model.predict(X, verbose=0)
probs = coral_probs_from_logits(logits)[0] # now 5 probabilities
pred_idx = int(np.argmax(probs))
pred_state = CLASSES[pred_idx]
return {
"input_ok": len(missing) == 0,
"missing": missing,
"z_scores": zscores_dict,
"probabilities": {CLASSES[i]: float(probs[i]) for i in range(len(CLASSES))},
"predicted_state": pred_state,
}
# ---------- Gradio interface ----------
def predict_from_json(payload, x_api_key: str = ""):
"""
Accepts 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):
return {"error": "Invalid payload: expected a JSON object mapping feature -> value."}
return predict_core(payload)
iface = gr.Interface(
fn=predict_from_json,
inputs=gr.JSON(label="ratios JSON (dict of feature -> value)"),
outputs="json",
title="Static Fingerprint Model API",
description=(
"POST JSON to /run/predict with a dict of your 21 ratios. "
"Server normalises using saved means/stds and returns probabilities + predicted state."
),
)
if __name__ == "__main__":
iface.launch() |