Update app.py

app.py

@@ -3,6 +3,10 @@ import tensorflow as tf
 import numpy as np
 from tensorflow.keras.preprocessing.image import load_img, img_to_array
 from PIL import Image
+from gtts import gTTS
+import os
+import tempfile
+import time
 
 # Load the trained model
 MODEL_PATH = "image_model.h5"
@@ -38,6 +42,13 @@ def predict_image(image):
 
     return CLASS_LABELS[predicted_class], confidence
 
+def speak(text):
+    tts = gTTS(text=text, lang='en')
+    with tempfile.NamedTemporaryFile(delete=False) as temp_file:
+        temp_file_path = temp_file.name + ".mp3"
+        tts.save(temp_file_path)
+        os.system(f"start {temp_file_path}")  # On Windows, use `start`, on Mac/Linux use `open`
+
 # Streamlit app
 st.title("Driver Distraction Detection")
 st.markdown("Team18 Image Project : Sayandip Bhattacharyya, Purnendu Rudrapal, Sridatta Das, Sidhartha Karjee")
@@ -56,6 +67,10 @@ if uploaded_file is not None:
         predicted_class, confidence = predict_image(resized_image)
 
     # Display the result
+    prediction_text = f"Class: {predicted_class}\nConfidence: {confidence:.2%}"
     st.subheader("Prediction")
-    st.write(f"**Class:** {predicted_class}")
-    st.write(f"**Confidence:** {confidence:.2%}")
+    st.write(prediction_text)
+
+    # Speak the prediction dynamically as text is displayed
+    speak(prediction_text)
+    time.sleep(2)  # Delay for audio to play before updating