app.py

import gradio as gr
import numpy as np
import tensorflow as tf
from tensorflow.keras.saving import load_model
import cv2

MODEL_PATH = "fer_surprise_softmax.keras"

# --------------------------
# 1) 모델 로딩
# --------------------------
print("📌 모델 로딩 중...")
model = load_model(MODEL_PATH, compile=False)
print("✅ 모델 로딩 완료!")


# --------------------------
# 2) 예측 함수 (실시간 웹캠)
# --------------------------
emotion_labels = ["angry", "disgust", "fear", "happy", "neutral", "sad", "surprise"]

def predict(frame, hr):
    if frame is None:
        return None, "카메라 프레임 없음"

    # 얼굴 탐지 (대비 강화 + grayscale)
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    resized = cv2.resize(gray, (96, 96))
    img = resized.reshape(1, 96, 96, 1).astype("float32") / 255.0

    # 모델 예측
    preds = model.predict(img, verbose=0)[0]
    top_idx = int(np.argmax(preds))
    top_label = emotion_labels[top_idx]
    surprise_score = float(preds[6])

    # Surprise 여부 binary
    surprise_flag = "Surprised 😲" if top_label == "surprise" else "Not Surprise 🙂"

    # 출력 구성
    result_text = (
        f"🧠 Top Emotion: {top_label}\n"
        f"😲 Surprise Probability: {surprise_score:.3f}\n"
        f"❤️ Heart Rate: {hr} BPM\n"
        f"📌 Surprise Detection: {surprise_flag}"
    )

    return frame, result_text


# --------------------------
# 3) Gradio UI
# --------------------------
with gr.Blocks() as demo:
    gr.Markdown("# 😲 Real-Time Surprise Detector + ❤️ Heart Rate Monitor")

    with gr.Row():
        webcam = gr.Image(
            sources=["webcam"],
            streaming=True,
            label="Webcam Input",
            height=380
        )
        hr = gr.Number(label="Heart Rate (from Arduino)", value=0)

    out_image = gr.Image(label="Live Feed")
    out_text = gr.Textbox(label="Analysis Result")

    webcam.stream(predict, inputs=[webcam, hr], outputs=[out_image, out_text])


demo.launch()