added pages folder to run mutipage apps

pages/1_📷️_Live_Stream.py

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+import av
+import os
+import sys
+import streamlit as st
+from streamlit_webrtc import VideoHTMLAttributes, webrtc_streamer
+from aiortc.contrib.media import MediaRecorder
+
+
+BASE_DIR = os.path.abspath(os.path.join(__file__, '../../'))
+sys.path.append(BASE_DIR)
+
+
+from utils import get_mediapipe_pose
+from process_frame import ProcessFrame
+from thresholds import get_thresholds_beginner, get_thresholds_pro
+
+
+st.title('AI Fitness Trainer: Squats Analysis')
+
+mode = st.radio('Select Mode', ['Beginner', 'Pro'], horizontal=True)
+
+thresholds = None 
+
+if mode == 'Beginner':
+    thresholds = get_thresholds_beginner()
+
+elif mode == 'Pro':
+    thresholds = get_thresholds_pro()
+
+
+live_process_frame = ProcessFrame(thresholds=thresholds, flip_frame=True)
+# Initialize face mesh solution
+pose = get_mediapipe_pose()
+
+
+output_video_file = f'output_live.flv'
+
+
+def video_frame_callback(frame: av.VideoFrame):
+    frame = frame.to_ndarray(format="rgb24")  # Decode and get RGB frame
+    frame, _ = live_process_frame.process(frame, pose)  # Process frame
+    return av.VideoFrame.from_ndarray(frame, format="rgb24")  # Encode and return BGR frame
+
+
+def out_recorder_factory() -> MediaRecorder:
+        return MediaRecorder(output_video_file)
+
+
+ctx = webrtc_streamer(
+                        key="Squats-pose-analysis",
+                        video_frame_callback=video_frame_callback,
+                        rtc_configuration={"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]},  # Add this config
+                        media_stream_constraints={"video": {"width": {"min":800, "max": 1280}}, "audio": False},
+                        video_html_attrs=VideoHTMLAttributes(autoPlay=True, controls=False, muted=False),
+                        out_recorder_factory=out_recorder_factory
+                    )
+
+
+if os.path.exists(output_video_file):
+    with open(output_video_file, 'rb') as op_vid:
+        st.download_button('Download Video', data = op_vid, file_name='output_live.flv')
+
+
+    
+    
+
+    
+
+

pages/2_ ⬆️_Upload_Video.py

@@ -0,0 +1,121 @@
+import av
+import os
+import sys
+import streamlit as st
+import cv2
+import tempfile
+
+
+BASE_DIR = os.path.abspath(os.path.join(__file__, '../../'))
+sys.path.append(BASE_DIR)
+
+
+from utils import get_mediapipe_pose
+from process_frame import ProcessFrame
+from thresholds import get_thresholds_beginner, get_thresholds_pro
+
+
+
+st.title('AI Fitness Trainer: Squats Analysis')
+
+mode = st.radio('Select Mode', ['Beginner', 'Pro'], horizontal=True)
+
+
+
+thresholds = None 
+
+if mode == 'Beginner':
+    thresholds = get_thresholds_beginner()
+
+elif mode == 'Pro':
+    thresholds = get_thresholds_pro()
+
+
+
+upload_process_frame = ProcessFrame(thresholds=thresholds)
+
+# Initialize face mesh solution
+pose = get_mediapipe_pose()
+
+
+output_video_file = f'output_recorded.mp4'
+
+
+with st.form('Upload', clear_on_submit=True):
+    up_file = st.file_uploader("Upload a Video", ['mp4','mov', 'avi'])
+    uploaded = st.form_submit_button("Upload")
+
+stframe = st.empty()
+
+ip_vid_str = '<p style="font-family:Helvetica; font-weight: bold; font-size: 16px;">Input Video</p>'
+warning_str = '<p style="font-family:Helvetica; font-weight: bold; color: Red; font-size: 17px;">Please Upload a Video first!!!</p>'
+
+warn = st.empty()
+
+
+download_button = st.empty()
+
+if up_file and uploaded:
+    
+    download_button.empty()
+    tfile = tempfile.NamedTemporaryFile(delete=False)
+
+    try:
+        warn.empty()
+        tfile.write(up_file.read())
+
+        vf = cv2.VideoCapture(tfile.name)
+
+        # ---------------------  Write the processed video frame. --------------------
+        fps = int(vf.get(cv2.CAP_PROP_FPS))
+        width = int(vf.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(vf.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        frame_size = (width, height)
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        video_output = cv2.VideoWriter(output_video_file, fourcc, fps, frame_size)
+        # -----------------------------------------------------------------------------
+
+        
+        txt = st.sidebar.markdown(ip_vid_str, unsafe_allow_html=True)   
+        ip_video = st.sidebar.video(tfile.name) 
+
+        while vf.isOpened():
+            ret, frame = vf.read()
+            if not ret:
+                break
+
+            # convert frame from BGR to RGB before processing it.
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            out_frame, _ = upload_process_frame.process(frame, pose)
+            stframe.image(out_frame)
+            video_output.write(out_frame[...,::-1])
+
+        
+        vf.release()
+        video_output.release()
+        stframe.empty()
+        ip_video.empty()
+        txt.empty()
+        tfile.close()
+    
+    except AttributeError:
+        warn.markdown(warning_str, unsafe_allow_html=True)   
+
+
+
+if os.path.exists(output_video_file):
+    with open(output_video_file, 'rb') as op_vid:
+        download_button.download_button('Download Video', data = op_vid, file_name='output_recorded.mp4')
+
+
+
+
+
+
+
+    
+    
+
+    
+
+