Create app.py

app.py

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+import cv2
+import numpy as np
+from tensorflow.keras.models import load_model
+import gradio as gr
+import tempfile
+import os
+
+# Load your pre-trained model
+model = load_model('cnn_lstm1.h5')
+
+# Function to preprocess each frame
+def preprocess_frame(frame):
+    resized_frame = cv2.resize(frame, (224, 224))  # Adjust size based on your model's input shape
+    normalized_frame = resized_frame / 255.0
+    return np.expand_dims(normalized_frame, axis=0)  # Add batch dimension
+
+# Function to process the video and make predictions
+def predict_drowsiness(video_path):
+    # Open the video file
+    cap = cv2.VideoCapture(video_path)
+    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    
+    # Create a temporary file for the output video
+    with tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) as temp_output:
+        temp_output_path = temp_output.name
+    
+    # Output video settings
+    out = cv2.VideoWriter(temp_output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (frame_width, frame_height))
+    
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        
+        # Preprocess frame
+        preprocessed_frame = preprocess_frame(frame)
+        
+        # Use the model to predict drowsiness
+        prediction = model.predict(preprocessed_frame)
+        drowsiness = np.argmax(prediction)
+        
+        # Add label to frame
+        label = 'Drowsy' if drowsiness == 0 else 'Alert'
+        cv2.putText(frame, label, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+        
+        # Write the frame with label to the output video
+        out.write(frame)
+    
+    # Release resources
+    cap.release()
+    out.release()
+    
+    return temp_output_path  # Return the path to the temporary output video
+
+# Gradio interface
+interface = gr.Interface(
+    fn=predict_drowsiness,
+    inputs=gr.Video(),  # Video input from webcam or upload
+    outputs="video",  # Return a playable video with predictions
+    title="Drowsiness Detection in Video",
+    description="Upload a video or record one, and this tool will detect if the person is drowsy.",
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()