app.py

import streamlit as st
import cv2
from PIL import Image
import time
import numpy as np

# Placeholder function for analyzing images and returning descriptions
def analyze_image(image):
    # Implement object recognition here
    # This function should return a list of descriptions for detected objects
    # For example:
    return ["chair on the left", "table in the center", "cat on the right"]

def main():
    st.title("Object Recognition Assistant for the Visually Impaired")

    # Setup webcam capture
    cap = cv2.VideoCapture(0)  # Use 0 for the default webcam

    FRAME_WINDOW = st.image([])
    last_time = time.time()

    while True:
        ret, frame = cap.read()
        if not ret:
            continue

        # Convert the image color to RGB
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        img = Image.fromarray(frame)

        # Display the current frame
        FRAME_WINDOW.image(img)

        # Check if 10 seconds have passed
        if time.time() - last_time > 10:
            last_time = time.time()

            # Analyze the image and get descriptions
            descriptions = analyze_image(img)

            # Display the descriptions
            st.write("Detected objects:")
            for desc in descriptions:
                st.write("- " + desc)

        time.sleep(0.1)

if __name__ == "__main__":
    main()