app.py

import streamlit as st
from streamlit_webrtc import webrtc_streamer, VideoTransformerBase, VideoFrame
import cv2
import numpy as np
from datetime import datetime
from keras.models import load_model
import sqlite3
import os

# Database Initialization
DB_NAME = "emotion_detection.db"

def initialize_database():
    conn = sqlite3.connect(DB_NAME)
    cursor = conn.cursor()
    cursor.execute("""
    CREATE TABLE IF NOT EXISTS face_data (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        name TEXT NOT NULL,
        emotion TEXT NOT NULL,
        timestamp TEXT NOT NULL
    )
    """)
    conn.commit()
    conn.close()

initialize_database()

# Load emotion detection model
@st.cache_resource
def load_emotion_model():
    return load_model('CNN_Model_acc_75.h5')

emotion_model = load_emotion_model()
emotion_labels = ['angry', 'fear', 'happy', 'neutral', 'sad', 'surprise']

# Video Transformer for Streamlit WebRTC
class EmotionDetector(VideoTransformerBase):
    def __init__(self):
        self.model = emotion_model

    def transform(self, frame: VideoFrame) -> VideoFrame:
        img = frame.to_ndarray(format="bgr24")
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        faces = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml").detectMultiScale(
            gray, scaleFactor=1.1, minNeighbors=5, minSize=(48, 48)
        )

        for (x, y, w, h) in faces:
            face = gray[y:y + h, x:x + w]
            face_resized = cv2.resize(face, (48, 48))
            face_normalized = face_resized / 255.0
            face_reshaped = np.reshape(face_normalized, (1, 48, 48, 1))
            
            prediction = self.model.predict(face_reshaped)
            emotion = emotion_labels[np.argmax(prediction[0])]
            
            # Draw bounding box and label
            cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
            cv2.putText(img, emotion, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)

        return VideoFrame.from_ndarray(img, format="bgr24")

# Sidebar menu
menu = st.sidebar.selectbox("Menu", ["Home", "View Records"])

if menu == "Home":
    st.title("Real-Time Emotion Detection")
    st.write("Using your camera for real-time emotion detection.")
    
    webrtc_streamer(
        key="emotion-detection",
        video_transformer_factory=EmotionDetector,
        media_stream_constraints={"video": True, "audio": False},
    )

elif menu == "View Records":
    st.title("View Records")
    st.subheader("Recent Records")
    
    conn = sqlite3.connect(DB_NAME)
    cursor = conn.cursor()
    cursor.execute("SELECT name, emotion, timestamp FROM face_data ORDER BY timestamp DESC LIMIT 5")
    records = cursor.fetchall()
    conn.close()

    for record in records:
        st.write(f"**Name**: {record[0]}, **Emotion**: {record[1]}, **Timestamp**: {record[2]}")