Update app.py

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 import cv2
 import mediapipe as mp
 import numpy as np
+from PIL import Image
 
 # Initialize mediapipe pose class
 mp_pose = mp.solutions.pose
@@ -117,20 +118,26 @@ def classify_pose(landmarks, output_image, display=False):
     return output_image, label
 
 def detect_and_classify_pose(input_image):
-    input_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
+    # Convert input to numpy array if it's not
+    if isinstance(input_image, Image.Image):
+        input_image = np.array(input_image)
+    
+    # Convert the image from RGB to BGR (OpenCV format)
+    input_image = cv2.cvtColor(input_image, cv2.COLOR_RGB2BGR)
     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 cv2.cvtColor(input_image, cv2.COLOR_RGB2BGR), pose_classification
+    return cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB), pose_classification
 
 iface = gr.Interface(
     fn=detect_and_classify_pose,
-    inputs=gr.Video(),
+    inputs=gr.Video(streaming=True),
     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()
+iface.launch(share=True)
+