Upload app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import json
+
+# ========== 1. Load model and stats once at startup ==========
+MODEL_PATH = "best_model.keras"
+STATS_PATH = "Means & Std for Excel.json"
+
+print("Loading model and stats...")
+model = tf.keras.models.load_model(MODEL_PATH)
+with open(STATS_PATH, "r") as f:
+    stats = json.load(f)
+
+# Feature order inferred from the JSON keys (keep stable)
+FEATURES = list(stats.keys())
+CLASSES = ["Normal", "Stressed", "Other"]  # adjust if your model uses different labels
+
+# ========== 2. Define prediction function ==========
+def predict_ratios(input_dict):
+    """
+    input_dict: dictionary of {indicator_name: value}
+    returns: dict with probabilities and predicted class
+    """
+    # Build z-score vector in model’s feature order
+    zscores = []
+    zscore_dict = {}
+
+    for feat in FEATURES:
+        val = float(input_dict.get(feat, 0))
+        mean = stats[feat]["mean"]
+        std = stats[feat]["std"]
+        z = 0.0 if std == 0 else (val - mean) / std
+        zscores.append(z)
+        zscore_dict[feat] = z
+
+    X = np.array([zscores], dtype=np.float32)
+
+    # Predict
+    probs = model.predict(X, verbose=0)[0]
+    pred_idx = int(np.argmax(probs))
+    pred_state = CLASSES[pred_idx]
+
+    return {
+        "predicted_state": pred_state,
+        "probabilities": {CLASSES[i]: float(probs[i]) for i in range(len(CLASSES))},
+        "z_scores": zscore_dict
+    }
+
+# ========== 3. Gradio interface ==========
+# Build input components automatically for manual testing in the browser.
+inputs = [gr.Number(label=f) for f in FEATURES]
+
+iface = gr.Interface(
+    fn=lambda *vals: predict_ratios({f: v for f, v in zip(FEATURES, vals)}),
+    inputs=inputs,
+    outputs="json",
+    title="Static Fingerprint Model API",
+    description="Enter ratios below or call via POST /run/predict"
+)
+
+# Launch app
+iface.launch()