general outline for app,py

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+
+# ... (TFLiteModel, preprocess_landmarks, make_prediction definitions as before) ...
+
+# Function to extract landmarks from the webcam frame
+def extract_landmarks_from_frame(frame):
+    # Your code to extract landmarks from the webcam frame using MediaPipe or any other method
+    # The landmark_data should be a list or array containing the spatial coordinates of landmarks
+    landmark_data = []  # Replace this with your actual landmark data
+    return landmark_data
+
+# Gradio Interface Function
+def predict_with_webcam(frame):
+    # Extract landmarks from the webcam frame
+    landmark_data = extract_landmarks_from_frame(frame)
+    if landmark_data is not None:
+        # Preprocess the landmarks
+        processed_landmarks = preprocess_landmarks(landmark_data)
+        # Make predictions using the TFLite model
+        predictions = make_prediction(processed_landmarks)
+        # You can process the predictions as needed before returning them
+        return predictions
+
+# Define the Gradio interface with the Webcam input and Text output
+webcam_interface = gr.Interface(
+    fn=predict_with_webcam,
+    inputs=gr.inputs.Webcam(),  # Use the Webcam input type
+    outputs="text",  # You can customize this based on your model's output
+    live=True,
+    interpretation="default",
+    title="Webcam Landmark Prediction",
+    description="Make predictions using landmarks extracted from your webcam stream.",
+)
+
+# Launch the Gradio app with the webcam interface
+if __name__ == "__main__":
+    webcam_interface.launch(share=True)