app.py

import numpy as np
import gradio as gr
from tensorflow.keras.models import load_model, Model
from hpelm import ELM
import joblib

# 🔄 Load scaler and ELM model
scaler = joblib.load("elm_scaler1.pkl")
elm = ELM(128, 1, classification='c')
elm.load("elm_model1.txt")

# ⚙️ Load trained MobileNet1D and create feature extractor
mobilenet = load_model("physio_model2.h5")
feature_extractor = Model(inputs=mobilenet.input, outputs=mobilenet.get_layer("penultimate_dense").output)

def preprocess_signal(ecg, eda, temp):
    """
    Combines input arrays into 3-channel format expected by MobileNet1D.
    All signals must be of same length (e.g., 1280).
    """
    ecg = np.array(ecg).reshape(-1, 1)
    eda = np.array(eda).reshape(-1, 1)
    temp = np.array(temp).reshape(-1, 1)
    return np.concatenate([ecg, eda, temp], axis=1)

def predict_stress(ecg, eda, temp):
    try:
        # 🚦 Preprocessing
        signal = preprocess_signal(ecg, eda, temp)  # Shape: (1280, 3)
        signal = signal.reshape(1, 1280, 3)

        # 🔍 Feature Extraction
        features = feature_extractor.predict(signal)

        # 🔄 Scaling for ELM
        features_scaled = scaler.transform(features)

        # 🔮 ELM Inference
        raw_pred = elm.predict(features_scaled)
        raw_pred = np.clip(raw_pred, -20, 20)
        prob = 1 / (1 + np.exp(-raw_pred))
        label = "Stress" if prob > 0.5 else "No Stress"

        return f"{label} (Confidence: {prob[0][0]:.2f})"
    
    except Exception as e:
        return f"❌ Error: {str(e)}"

# 🧪 Gradio demo interface
inputs = [
    gr.Textbox(label="ECG Signal (comma-separated)", lines=2, placeholder="e.g. 0.1,0.12,...,0.13"),
    gr.Textbox(label="EDA Signal (comma-separated)", lines=2, placeholder="e.g. 0.2,0.18,...,0.15"),
    gr.Textbox(label="Temperature Signal (comma-separated)", lines=2, placeholder="e.g. 32.1,32.2,...,32.5")
]

def parse_and_predict(ecg_str, eda_str, temp_str):
    try:
        ecg = list(map(float, ecg_str.strip().split(',')))
        eda = list(map(float, eda_str.strip().split(',')))
        temp = list(map(float, temp_str.strip().split(',')))
        
        if len(ecg) != 1280 or len(eda) != 1280 or len(temp) != 1280:
            return "❌ Each signal must be exactly 1280 samples long."
        
        return predict_stress(ecg, eda, temp)
    
    except:
        return "❌ Invalid input format. Use comma-separated values only."

demo = gr.Interface(fn=parse_and_predict, inputs=inputs, outputs="text", title="Stress Detection via Physio Signals",
                    description="Upload ECG, EDA, and Temperature signals (1280 samples each) to detect stress using MobileNet1D + ELM")

# 🏁 Run app
if __name__ == "__main__":
    demo.launch(
    server_name="0.0.0.0", 
    server_port=7860, 
    share=True, 
    show_api=True
    )