app.py

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

# Load the saved model
loaded_model = load_model("accident_detection_model.h5")
st.title("Accident Detection")

# Checkbox to enable camera input
camera_checkbox = st.checkbox("Use Camera")

if camera_checkbox:
    

    image = st.camera_input("Take Photo")

    if(st.button("Capture")):

    # Preprocess the image
        image = Image.open(image)
        img_array = np.array(image.resize((150, 150)))
        img_array = img_array / 255.0
        img_array = np.expand_dims(img_array, axis=0)

        # Classify the image
        prediction = loaded_model.predict(img_array)
        if prediction[0] > 0.5:
            st.write("Prediction: Accident")
        else:
            st.write("Prediction: Non-Accident")


else:
    # Upload image
    uploaded_file = st.file_uploader("Choose an image...", type="jpg")

    if uploaded_file is not None:
        image = Image.open(uploaded_file)
        st.image(image, caption='Uploaded Image.', use_column_width=True)
        st.write("")
        st.write("Classifying...")

        # Preprocess the image
        img_array = np.array(image.resize((150, 150)))
        img_array = img_array / 255.0
        img_array = np.expand_dims(img_array, axis=0)

        # Classify the image
        prediction = loaded_model.predict(img_array)
        if prediction[0] > 0.5:
            st.write("Prediction: Accident")
        else:
            st.write("Prediction: Non-Accident")