Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import cv2
+
+# Load model
+model = tf.keras.models.load_model("keras_model.h5")
+
+# Replace this with your actual label list if you have it
+labels = ["Good Posture", "Bad Posture"]
+
+def predict_from_webcam(frame):
+    # Resize frame to match model input size (usually 224x224 for Teachable Machine)
+    img = cv2.resize(frame, (224, 224))
+    img = img / 255.0  # normalize
+    img = np.expand_dims(img, axis=0)  # batch dimension
+
+    # Predict
+    prediction = model.predict(img)[0]
+    top_label = labels[np.argmax(prediction)]
+    confidence = np.max(prediction)
+
+    return f"{top_label} ({confidence*100:.2f}%)"
+
+# Gradio interface
+demo = gr.Interface(
+    fn=predict_from_webcam,
+    inputs=gr.Image(source="webcam", streaming=True),
+    outputs=gr.Text(),
+    live=True,
+    title="Posture Detector",
+    description="Detects if your posture is good or bad using a webcam model trained with Teachable Machine."
+)
+
+demo.launch()