Create app.py

app.py

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+import streamlit as st
+import cv2
+import joblib
+import mediapipe as mp
+import numpy as np
+from streamlit_webrtc import webrtc_streamer, VideoTransformerBase
+
+# Load the trained model and label encoder
+model = joblib.load("pose_classifier.joblib")
+label_encoder = joblib.load("label_encoder.joblib")
+
+# Initialize MediaPipe Pose
+mp_pose = mp.solutions.pose
+pose = mp_pose.Pose()
+
+class PoseTransformer(VideoTransformerBase):
+    def transform(self, frame):
+        img = frame.to_ndarray(format="bgr24")  # Convert to BGR format
+        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+        # Process frame with MediaPipe Pose
+        results = pose.process(img_rgb)
+
+        if results.pose_landmarks:
+            landmarks = results.pose_landmarks.landmark
+            pose_data = [j.x for j in landmarks] + [j.y for j in landmarks] + \
+                        [j.z for j in landmarks] + [j.visibility for j in landmarks]
+
+            pose_data = np.array(pose_data).reshape(1, -1)
+
+            # Predict pose
+            y_pred = model.predict(pose_data)
+            predicted_label = label_encoder.inverse_transform(y_pred)[0]
+
+            # Display predicted label
+            cv2.putText(img, f"Pose: {predicted_label}", (20, 50),
+                        cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 3)
+
+        return cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+
+# Streamlit UI
+st.title("Live Pose Classification")
+st.write("This application detects human poses in real-time.")
+
+# Start WebRTC Stream
+webrtc_streamer(key="pose-detection", video_transformer_factory=PoseTransformer)