Update app.py

app.py

@@ -3,9 +3,12 @@ import numpy as np
 import tensorflow as tf
 from tensorflow.keras.preprocessing import image
 import time
+from gtts import gTTS
+import tempfile
+import base64
 
 # Load the Keras model (.keras file)
-model = tf.keras.models.load_model("Model.keras")  
+model = tf.keras.models.load_model("Model.keras")
 
 # Image parameters for prediction
 img_height, img_width = 128, 128
@@ -20,13 +23,27 @@ def predict(img):
     
     # Make prediction using the model
     prediction = model.predict(img_array)
-    label = "The eyes are visibly closed, hinting at a danger ahead for driver." if prediction[0] > 0.5 else "The eyes are visibly open, hinting at Driver's alertness."
+    label = "The eyes are visibly closed, hinting at a danger ahead for the driver." if prediction[0] > 0.5 else "The eyes are visibly open, hinting at the driver's alertness."
     
     # Calculate inference time
     inference_time = time.time() - start_time
     
     return label, round(inference_time, 4)  # Return label and inference time
 
+# Function to convert text to speech and auto-play
+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("Eye State Prediction")
 st.write("Team 18: Sayandip Bhattacharyya, Purnendu Rudrapal, Sridatta Das, Sidhartha Karjee")
@@ -45,3 +62,6 @@ if uploaded_image is not None:
     # Display results
     st.write(f"Prediction: **{label}**")
     st.write(f"Inference Time: **{inference_time} seconds**")
+
+    # Convert the result into speech and play it
+    speak_auto(f"{label} Inference time: {inference_time} seconds.")