app.py

import cv2
import streamlit as st
import numpy as np
from keras.models import load_model

# Load the pre-trained model
model = load_model('keras_model.h5')

# Load the class labels
with open('labels.txt', 'r') as f:
    class_names = [line.strip() for line in f.readlines()]

# Load the Haar cascade classifier for face detection
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

font = cv2.FONT_HERSHEY_SIMPLEX

# Use this line to capture video from the webcam
cap = cv2.VideoCapture(3)

# Set the title for the Streamlit app
st.title("Video Capture with OpenCV")

frame_placeholder = st.empty()

while True:
    ret, frame = cap.read()
    # Detect faces in the frame
    faces = face_cascade.detectMultiScale(frame, scaleFactor=1.3, minNeighbors=5)
    if not ret:
        st.write("The video capture has ended.")
        break

    # You can process the frame here if needed
    # e.g., apply filters, transformations, or object detection
    for (x, y, w, h) in faces:
            # Extract the face region
            face_img = frame[y:y+h, x:x+w]

            # Preprocess the face image
            face_img = cv2.resize(face_img, (224, 224))
            face_img = np.expand_dims(face_img, axis=0)
            face_img = face_img / 255.0

            # Predict the class probabilities
            pred_probs = model.predict(face_img)[0]
            class_idx = np.argmax(pred_probs)
            class_prob = pred_probs[class_idx]

            # Get the class name and display it on the image
            class_name = class_names[class_idx]
            if class_prob*100 < 70:
                cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)
                text = '{}: {:.2f}%'.format('Unknown', class_prob * 100)
                cv2.putText(frame, text, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
            else:
                cv2.putText(frame, class_name, (x, y - 10), font, 1, (0, 255, 0), 2)
                cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
                text = '{}: {:.2f}%'.format(class_name, class_prob * 100)
                cv2.putText(frame, text, (x, y + h + 30), font, 0.5, (0, 255, 0), 1)
    # Convert the frame from BGR to RGB format
    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

    # Display the frame using Streamlit's st.image
    frame_placeholder.image(frame, channels="RGB")

    # Break the loop if the 'q' key is pressed
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

cap.release()
cv2.destroyAllWindows()