Create app.py

app.py

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+import streamlit as st
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+from PIL import Image
+
+# Load the trained model
+MODEL_PATH = "image_model.h5"
+model = tf.keras.models.load_model(MODEL_PATH)
+
+# Define image dimensions
+IMG_WIDTH, IMG_HEIGHT = 64, 48
+
+# Class labels with descriptions
+CLASS_LABELS = {
+    0: "Normal driving (safe)",
+    1: "Texting - right",
+    2: "Talking on the phone - right",
+    3: "Texting - left",
+    4: "Talking on the phone - left",
+    5: "Operating the radio",
+    6: "Drinking",
+    7: "Reaching behind",
+    8: "Hair and makeup",
+    9: "Talking to passenger"
+}
+
+def predict_image(image):
+    """
+    Predict the class of a given image.
+
+    Args:
+        image (PIL.Image.Image): Input image.
+
+    Returns:
+        str: Predicted class description.
+        float: Confidence score of the prediction.
+    """
+    # Preprocess the image
+    img_array = img_to_array(image)
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    img_array = img_array / 255.0  # Normalize pixel values to [0, 1]
+
+    # Make a prediction
+    predictions = model.predict(img_array)
+    predicted_class = np.argmax(predictions, axis=1)[0]
+    confidence = np.max(predictions)
+
+    return CLASS_LABELS[predicted_class], confidence
+
+# Streamlit app
+st.title("Driver Distraction Detection")
+st.markdown("Team18 Image Project : Sayandip Bhattacharyya, Purnendu Rudrapal, Sridatta Das, Siddharta Karjee")
+
+# File upload
+uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    image = Image.open(uploaded_file).convert("RGB")
+    resized_image = image.resize((IMG_WIDTH, IMG_HEIGHT))
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Predict the class
+    with st.spinner("Predicting..."):
+        predicted_class, confidence = predict_image(resized_image)
+
+    # Display the result
+    st.subheader("Prediction")
+    st.write(f"**Class:** {predicted_class}")
+    st.write(f"**Confidence:** {confidence:.2%}")