Update app.py

app.py

@@ -5,7 +5,7 @@ import numpy as np
 
 # Initialize mediapipe pose class
 mp_pose = mp.solutions.pose
-pose = mp_pose.Pose(static_image_mode=False, min_detection_confidence=0.5, model_complexity=1)
+pose = None
 mp_drawing = mp.solutions.drawing_utils
 
 # Function to calculate the angle between three points
@@ -26,9 +26,6 @@ def calculate_angle(a, b, c):
 def classify_pose(landmarks, output_image, display=False):
     label = 'Unknown Pose'
 
-
-    color = (0, 0, 255)
-
     # Calculate the required angles
     left_elbow_angle = calculate_angle(
         landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value],
@@ -60,51 +57,12 @@ def classify_pose(landmarks, output_image, display=False):
         landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value],
         landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value])
 
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     # Check for Five-Pointed Star Pose
     if abs(landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y - landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y) < 0.1 and \
        abs(landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].y - landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].y) < 0.1 and \
        abs(landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x - landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].x) > 0.2 and \
        abs(landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x - landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].x) > 0.2:
         label = "Five-Pointed Star Pose"
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     # Check for Warrior II pose
     if 165 < left_elbow_angle < 195 and 165 < right_elbow_angle < 195 and \
@@ -133,49 +91,4 @@ def classify_pose(landmarks, output_image, display=False):
         label = "Upward Salute Pose"
 
     # Check for Hands Under Feet Pose
-    if landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y > landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y and \
-       landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].y > landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].y and \
-       abs(landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x - landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x) < 0.05 and \
-       abs(landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].x - landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].x) < 0.05:
-        label = "Hands Under Feet Pose"
-
-    # Check for Plank Pose
-
-    # The body should be in a straight line from head to heels, 
-    # so the shoulder and hip angles should be close to 180 degrees
-    if left_shoulder_angle > 160 and left_shoulder_angle < 200 and \
-       right_shoulder_angle > 160 and right_shoulder_angle < 200 and \
-       left_knee_angle > 160 and left_knee_angle < 200 and \
-       right_knee_angle > 160 and right_knee_angle < 200:
-        label = "Plank Pose"
-    
-    # Update the color to green if pose is classified
-
-    if label != 'Unknown Pose':
-
-
-        color = (0, 255, 0)
-    
-    # Write the label on the output image
-    cv2.putText(output_image, label, (10, 30), cv2.FONT_HERSHEY_PLAIN, 2, color, 2)
-
-    return output_image, label
-
-def detect_and_classify_pose(input_image):
-    input_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
-    results = pose.process(input_image)
-    pose_classification = "No pose detected"
-    if results.pose_landmarks:
-        mp_drawing.draw_landmarks(input_image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
-        input_image, pose_classification = classify_pose(results.pose_landmarks.landmark, input_image)
-    return input_image, pose_classification
-
-iface = gr.Interface(
-    fn=detect_and_classify_pose,
-    inputs="image",
-    outputs=["image", "text"],
-    title="Live Yoga Pose Detection and Classification",
-    description="This app detects and classifies yoga poses from the live camera feed using MediaPipe.",
-)
-
-iface.launch()
+    if landmarks[mp_pose.Pose