app.py

import streamlit as st
import cv2
import numpy as np
import time
import os
from keras.models import load_model
from PIL import Image
import pymongo
from datetime import datetime
import tempfile
from facenet_pytorch import MTCNN

# MongoDB Atlas Connection String
MONGO_URI = "mongodb+srv://test:[email protected]/?retryWrites=true&w=majority"

# Connect to MongoDB
client = pymongo.MongoClient(MONGO_URI)
db = client.get_database("emotion_detection")
collection = db.get_collection("face_data")

# Larger title
st.markdown("<h1 style='text-align: center;'>Emotion Detection with Face Recognition</h1>", unsafe_allow_html=True)

# Smaller subtitle
st.markdown("<h3 style='text-align: center;'>angry, fear, happy, neutral, sad, surprise</h3>", unsafe_allow_html=True)

# Start time for measuring performance
start = time.time()

# Load the emotion detection model
@st.cache_resource
def load_emotion_model():
    model = load_model('CNN_Model_acc_75.h5')  # Ensure this file is in your Space
    return model

model = load_emotion_model()
print("Time taken to load model: ", time.time() - start)

# Emotion labels
emotion_labels = ['angry', 'fear', 'happy', 'neutral', 'sad', 'surprise']

# Initialize MTCNN for face detection
mtcnn = MTCNN()

# Load known faces and names
known_faces = []
known_names = []
face_recognizer = cv2.face.LBPHFaceRecognizer_create()

def load_known_faces():
    folder_path = "known_faces"  # Folder containing known face images
    for image_name in os.listdir(folder_path):
        if image_name.endswith(('.jpg', '.jpeg', '.png')): 
            image_path = os.path.join(folder_path, image_name)
            image = cv2.imread(image_path)
            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
            # Detect face in the image using mtcnn
            faces, _ = mtcnn.detect(image)  # Use the correct method detect()

            if faces is not None:
                for face in faces:
                    x, y, w, h = face[0], face[1], face[2], face[3]
                    roi_gray = gray[y:y+h, x:x+w]
                    known_faces.append(roi_gray)
                    known_names.append(image_name.split('.')[0])  # Assuming file name is the person's name

    # Train the recognizer with the known faces
    face_recognizer.train(known_faces, np.array([i for i in range(len(known_faces))]))

load_known_faces()

# Process a single frame
def process_frame(frame):
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces, _ = mtcnn.detect(frame)  # Use the correct detect method

    result_text = ""  # Initialize result text

    if faces is not None and len(faces) > 0:
        for face in faces:
            x, y, w, h = face[0], face[1], face[2], face[3]
            roi_color = frame[y:y+h, x:x+w]
            roi_gray = gray[y:y+h, x:x+w]

            # Apply histogram equalization for better feature extraction
            roi_gray = cv2.equalizeHist(roi_gray)

            face_roi = cv2.resize(roi_color, (48, 48))
            face_roi = cv2.cvtColor(face_roi, cv2.COLOR_BGR2RGB)
            face_roi = np.expand_dims(face_roi, axis=0) / 255.0  # Normalize

            # Emotion detection
            predictions = model.predict(face_roi)
            emotion = emotion_labels[np.argmax(predictions[0])]

            # Face recognition
            name = "Unknown"
            label, confidence = face_recognizer.predict(roi_gray)
            if confidence < 100:
                name = known_names[label]

            # Format result text
            result_text = f"{name} is feeling {emotion}"

            # Save data to MongoDB if face is recognized (name != Unknown)
            if name != "Unknown":
                timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
                document = {
                    "name": name,
                    "emotion": emotion,
                    "timestamp": timestamp
                }
                # Insert the data into MongoDB
                collection.insert_one(document)
                print(f"Data inserted into MongoDB: {document}")

            # Draw bounding box and label
            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
            cv2.putText(frame, result_text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)

    return frame, result_text

# Video feed display
def video_feed(video_source):
    frame_placeholder = st.empty()  # Placeholder for displaying video frames
    text_placeholder = st.empty()  # Placeholder for displaying result text

    while True:
        ret, frame = video_source.read()
        if not ret:
            break

        frame, result_text = process_frame(frame)

        # Display frame and result text
        frame_placeholder.image(frame, channels="BGR", use_column_width=True)
        text_placeholder.markdown(f"<h3 style='text-align: center;'>{result_text}</h3>", unsafe_allow_html=True)

# Sidebar for user input source selection
upload_choice = st.sidebar.radio("Choose Input Source", ["Upload Image", "Upload Video", "Camera"])

if upload_choice == "Camera":
    image = st.camera_input("Take a picture")

    if image:
        frame = np.array(Image.open(image))
        frame, result_text = process_frame(frame)
        st.image(frame, caption='Processed Image', use_column_width=True)
        st.markdown(f"<h3 style='text-align: center;'>{result_text}</h3>", unsafe_allow_html=True)

elif upload_choice == "Upload Image":
    uploaded_image = st.file_uploader("Upload Image", type=["png", "jpg", "jpeg", "gif"])

    if uploaded_image:
        image = Image.open(uploaded_image)
        frame = np.array(image)
        frame, result_text = process_frame(frame)
        st.image(frame, caption='Processed Image', use_column_width=True)
        st.markdown(f"<h3 style='text-align: center;'>{result_text}</h3>", unsafe_allow_html=True)

elif upload_choice == "Upload Video":
    uploaded_video = st.file_uploader("Upload Video", type=["mp4", "mov", "avi", "mkv", "webm"])

    if uploaded_video:
        with tempfile.NamedTemporaryFile(delete=False) as tfile:
            tfile.write(uploaded_video.read())
            video_source = cv2.VideoCapture(tfile.name)
            video_feed(video_source)

# Display the records stored in MongoDB with latest records on top
st.markdown("### MongoDB Records")
records = collection.find().sort("timestamp", -1)  # Sort records by timestamp in descending order

for record in records:
    col1, col2, col3 = st.columns([3, 3, 1])
    with col1:
        st.write(f"**Name**: {record['name']}")
    with col2:
        st.write(f"**Emotion**: {record['emotion']}")
    with col3:
        st.write(f"**Timestamp**: {record['timestamp']}")
    
    # Delete record button
    delete_button = st.button(f"Delete {record['_id']}", key=record['_id'])
    if delete_button:
        collection.delete_one({"_id": record["_id"]})
        st.success(f"Record with ID {record['_id']} has been deleted.")