app.py

import streamlit as st
import tensorflow as tf
from PIL import Image
import numpy as np
import time
from gtts import gTTS
import tempfile
import base64

# Load the trained model
@st.cache_resource
def load_model():
    model = tf.keras.models.load_model("Drowsiness Detection Model.keras")
    return model

model = load_model()

# Preprocessing function
def preprocess_image(image):
    # Convert to RGB to ensure 3 channels
    image = image.convert("RGB")
    image = image.resize((224, 224))
    image_array = np.array(image) / 255.0
    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
    return image_array

# Function to play speech
def speak_auto(text):
    tts = gTTS(text=text, lang='en')
    with tempfile.NamedTemporaryFile(delete=True, suffix=".mp3") as fp:
        tts.save(fp.name)
        audio_bytes = fp.read()
        b64 = base64.b64encode(audio_bytes).decode()
        audio_html = f"""
            <audio autoplay>
                <source src="data:audio/mp3;base64,{b64}" type="audio/mp3">
            </audio>
        """
        st.markdown(audio_html, unsafe_allow_html=True)

# Streamlit UI
st.title("🚦 Drowsiness Detection Model")
st.write("Team 18 Project: Sayandip Bhattacharyya, Sidhartha Karjee, Sridatta Das, Purnendu Rudrapal")

uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    # Display uploaded image
    image = Image.open(uploaded_file)
    st.image(image, caption="Uploaded Image")

    # Preprocess and predict
    start_time = time.time()
    
    image_array = preprocess_image(image)
    prediction = model.predict(image_array)[0][0]
    elapsed_time = time.time() - start_time
    
    # Display results
    label = "Drowsy" if prediction > 0.5 else "Non-Drowsy"
    confidence = prediction if label == "Drowsy" else 1 - prediction

    st.write(f"### Prediction: **{label}**")
    st.write(f"Confidence: **{confidence:.2%}**")
    st.write(f"⏱️ Inference Time: **{elapsed_time:.4f} seconds**")

    # Call speak_auto function to speak out the prediction
    prediction_text = f"The person in the uploaded image is {label} with a prediction confidence of {confidence:.2%}."
    speak_auto(prediction_text)  # Auto-play speech