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README.md

@@ -1,13 +1 @@
----
-title: Wokrout Eaasz
-emoji: 🐠
-colorFrom: red
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.40.1
-app_file: app.py
-pinned: false
-short_description: 'corrects your pushup form and squat form '
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# squat_easz

ltt.json

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main.py

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+import streamlit as st
+import json
+from streamlit_lottie import st_lottie
+
+# Load Lottie animation from a JSON file
+def load_lottiefile(filepath: str):
+    with open(filepath, "r") as f:
+        return json.load(f)
+st.set_page_config(layout="wide")
+
+st.markdown("<h1 style='text-align: center;'>Workout Easz</h1>", unsafe_allow_html=True)
+lottie_animation = load_lottiefile("ltt.json")
+st_lottie(lottie_animation, height=300, key="header_animation")
+# Disclaimer
+st.markdown("""
+<div style="text-align: center; color: #FF0000;">
+    <strong>Disclaimer:</strong> 
+    The performance of pose detection depends on the model's accuracy. As of now, our application uses a model with approximately 50% pose detection accuracy. Results may vary based on the quality of the video input and the model's limitations.
+</div>
+""", unsafe_allow_html=True)
+# Introduction and Key Points
+with st.expander("Introduction to MediaPipe"):
+    st.header("Introduction to MediaPipe")
+    st.write("""
+    MediaPipe is an open-source framework developed by Google for building multimodal machine learning pipelines. Its Pose solution is designed for high-fidelity body pose tracking, detecting 33 key body landmarks in real-time[1][2].
+    """)
+
+with st.expander("Extraction of 33 Key Points"):
+    st.header("Extraction of 33 Key Points")
+    st.write("""
+    MediaPipe Pose detects 33 key body landmarks, each with x, y, z coordinates, and a visibility score. These landmarks represent various parts of the body, such as the nose, eyes, ears, shoulders, elbows, wrists, hips, knees, ankles, and feet[1][2].
+    """)
+    st.image("pose_landmarks_index.png", caption="MediaPipe Pose Landmarks", width=400)
+
+# Squat Analysis
+squat_expander = st.expander("Squat Form Analysis")
+with squat_expander:
+    st.header("Squat Form Analysis")
+
+    # Key Landmarks for Squat Analysis
+    st.subheader("1. Key Landmarks for Squat Analysis")
+    st.write("""
+    For squat analysis, we focus on the following key landmarks:
+    - Shoulder (left and right)
+    - Hip (left and right)
+    - Knee (left and right)
+    - Ankle (left and right)
+
+    These landmarks are essential for calculating the joint angles critical for analyzing squat form[3].
+    """)
+
+    # Calculation of Joint Angles for Squat
+    st.subheader("2. Calculation of Joint Angles for Squat")
+
+    # Knee Angle
+    st.write("**a. Knee Angle**")
+    st.write("""
+    The knee angle is formed by the hip, knee, and ankle. It is calculated using the arctangent function:
+    """)
+    st.latex(r"\theta_{\text{knee}} = \left| \frac{180}{\pi} \times \left( \arctan2(y_{\text{ankle}} - y_{\text{knee}}, x_{\text{ankle}} - x_{\text{knee}}) - \arctan2(y_{\text{hip}} - y_{\text{knee}}, x_{\text{hip}} - x_{\text{knee}}) \right) \right|")
+
+    # Hip Angle
+    st.write("**b. Hip Angle**")
+    st.write("""
+    The hip angle is formed by the shoulder, hip, and knee. It is calculated similarly:
+    """)
+    st.latex(r"\theta_{\text{hip}} = \left| \frac{180}{\pi} \times \left( \arctan2(y_{\text{knee}} - y_{\text{hip}}, x_{\text{knee}} - x_{\text{hip}}) - \arctan2(y_{\text{shoulder}} - y_{\text{hip}}, x_{\text{shoulder}} - x_{\text{hip}}) \right) \right|")
+
+    # Back Angle
+    st.write("**c. Back Angle**")
+    st.write("""
+    The back angle is formed by the shoulder, hip, and ankle:
+    """)
+    st.latex(r"\theta_{\text{back}} = \left| \frac{180}{\pi} \times \left( \arctan2(y_{\text{ankle}} - y_{\text{hip}}, x_{\text{ankle}} - x_{\text{hip}}) - \arctan2(y_{\text{shoulder}} - y_{\text{hip}}, x_{\text{shoulder}} - x_{\text{hip}}) \right) \right|")
+
+    # Form Issue Detection for Squat
+    st.subheader("3. Form Issue Detection for Squat")
+
+    st.write("**Ideal Angle Ranges:**")
+    st.write("""
+    - Knee Angle: 90 to 110 degrees
+    - Hip Angle: 80 to 100 degrees
+    - Back Angle: 70 to 90 degrees
+    """)
+
+    st.write("**Detection of Deviations:**")
+    st.write("""
+    - Knees Bending Too Much: Knee angle < 90 degrees
+    - Hips Bending Too Much: Hip angle < 80 degrees
+    - Back Leaning Too Much: Back angle < 70 degrees
+    """)
+
+    # Providing Suggestions for Squat
+    st.subheader("4. Providing Suggestions for Squat")
+    st.write("""
+    Based on the detected form issues, specific suggestions are provided:
+
+    1. Knees Bending Too Much:
+       - Suggestion: "Watch your knee bend. Ensure your knees do not bend excessively and maintain proper alignment with your toes."
+
+    2. Hips Bending Too Much:
+       - Suggestion: "Keep your hips higher. Avoid excessive hip bending by maintaining a more upright posture."
+
+    3. Back Leaning Too Much:
+       - Suggestion: "Maintain a straighter back. Focus on keeping your back straight and avoid leaning forward excessively."
+    """)
+
+# Push-up Analysis
+pushup_expander = st.expander("Push-up Form Analysis")
+with pushup_expander:
+    st.header("Push-up Form Analysis")
+
+    # Key Landmarks for Push-up Analysis
+    st.subheader("1. Key Landmarks for Push-up Analysis")
+    st.write("""
+    For push-up analysis, we focus on the following key landmarks:
+    - Shoulder (left and right)
+    - Elbow (left and right)
+    - Wrist (left and right)
+    - Hip (left and right)
+    - Ankle (left and right)
+
+    These landmarks are crucial for calculating the joint angles and body alignment in push-ups[3].
+    """)
+
+    # Calculation of Joint Angles for Push-up
+    st.subheader("2. Calculation of Joint Angles for Push-up")
+
+    # Elbow Angle
+    st.write("**a. Elbow Angle**")
+    st.write("""
+    The elbow angle is formed by the shoulder, elbow, and wrist. It is calculated using:
+    """)
+    st.latex(r"\theta_{\text{elbow}} = \left| \frac{180}{\pi} \times \left( \arctan2(y_{\text{wrist}} - y_{\text{elbow}}, x_{\text{wrist}} - x_{\text{elbow}}) - \arctan2(y_{\text{shoulder}} - y_{\text{elbow}}, x_{\text{shoulder}} - x_{\text{elbow}}) \right) \right|")
+
+    # Body Alignment Angle
+    st.write("**b. Body Alignment Angle**")
+    st.write("""
+    The body alignment angle is formed by the shoulder, hip, and ankle. It is calculated as:
+    """)
+    st.latex(r"\theta_{\text{alignment}} = \left| \frac{180}{\pi} \times \left( \arctan2(y_{\text{ankle}} - y_{\text{hip}}, x_{\text{ankle}} - x_{\text{hip}}) - \arctan2(y_{\text{shoulder}} - y_{\text{hip}}, x_{\text{shoulder}} - x_{\text{hip}}) \right) \right|")
+
+    # Form Issue Detection for Push-up
+    st.subheader("3. Form Issue Detection for Push-up")
+
+    st.write("**Ideal Angle Ranges:**")
+    st.write("""
+    - Elbow Angle: 90 to 110 degrees (at the bottom of the push-up)
+    - Body Alignment Angle: 170 to 180 degrees (straight body)
+    """)
+
+    st.write("**Detection of Deviations:**")
+    st.write("""
+    - Incomplete Push-up: Elbow angle > 110 degrees at the bottom
+    - Elbow Flaring: Elbow angle < 90 degrees
+    - Sagging Hips: Body alignment angle < 170 degrees
+    """)
+
+    # Providing Suggestions for Push-up
+    st.subheader("4. Providing Suggestions for Push-up")
+    st.write("""
+    Based on the detected form issues, specific suggestions are provided:
+
+    1. Incomplete Push-up:
+       - Suggestion: "Lower your body further. Aim to bring your chest closer to the ground for a full range of motion."
+
+    2. Elbow Flaring:
+       - Suggestion: "Keep your elbows closer to your body. Tuck them in to engage your chest and triceps more effectively."
+
+    3. Sagging Hips:
+       - Suggestion: "Maintain a straight body line. Engage your core to keep your hips aligned with your shoulders and ankles."
+    """)
+
+# Conclusion
+with st.expander("Conclusion",expanded=False):
+    st.write("""
+    By leveraging MediaPipe's pose estimation capabilities, this application can effectively analyze both squat and push-up forms. It calculates key joint angles and body alignments, compares them to ideal ranges, and provides specific suggestions for improvement. This approach enables users to receive real-time feedback on their exercise technique, helping them to perform exercises correctly and reduce the risk of injury[1][2][3].
+""")
+# Version Updates
+with st.expander("Version Updates", expanded=False):
+    st.header("Version Updates")
+
+    st.subheader("Current Version")
+    st.write("""
+    **Current Update:** As of today, our application now includes both squat and push-up form analysis.
+    """)
+
+    st.subheader("Previous Versions")
+    st.write("""
+    **Initial Release:** Squat workout analysis, released on July 19th.
+    """)
+
+    st.write("""
+    Stay tuned for more updates as we continue to enhance our application's features and performance.
+    """)
+# References
+with st.expander("References"):
+    st.header("References")
+    st.write("""
+[1] [MediaPipe Pose Documentation](https://github.com/google-ai-edge/mediapipe/blob/master/docs/solutions/pose.md)
+[2] [Google AI Edge - MediaPipe Pose Landmarker](https://ai.google.dev/edge/mediapipe/solutions/vision/pose_landmarker)
+[3] [Real-time Human Pose Estimation using MediaPipe](https://sigmoidal.ai/en/real-time-human-pose-estimation-using-mediapipe/)
+[4] [Kaggle Dataset for Push-Up Analysis](https://www.kaggle.com/datasets/mohamadashrafsalama/pushup)
+""")
+

packages.txt

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+libgl1-mesa-glx
+libglib2.0-0

pages/pushup.py

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+import streamlit as st
+import cv2
+import mediapipe as mp
+import numpy as np
+import time
+import json
+
+# Cache the MediaPipe Pose model
+@st.cache_resource
+def load_pose_model():
+    mp_pose = mp.solutions.pose
+    pose = mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5)
+    return mp_pose, pose
+
+mp_pose, pose = load_pose_model()
+mp_drawing = mp.solutions.drawing_utils
+
+# Function to calculate angle
+def calculate_angle(a, b, c):
+    a = np.array(a)  # First point
+    b = np.array(b)  # Mid point
+    c = np.array(c)  # End point
+    radians = np.arctan2(c[1] - b[1], c[0] - b[0]) - np.arctan2(a[1] - b[1], a[0] - b[0])
+    angle = np.abs(radians * 180.0 / np.pi)
+    if angle > 180.0:
+        angle = 360 - angle
+    return angle
+
+# Function to calculate body angle
+def calculate_body_angle(shoulder, hip, ankle):
+    shoulder = np.array(shoulder)
+    hip = np.array(hip)
+    ankle = np.array(ankle)
+    radians = np.arctan2(ankle[1] - hip[1], ankle[0] - hip[0]) - np.arctan2(shoulder[1] - hip[1], shoulder[0] - hip[0])
+    angle = np.abs(radians * 180.0 / np.pi)
+    if angle > 180.0:
+        angle = 360 - angle
+    return angle
+
+# Function to calculate rep score
+def calculate_rep_score(elbow_angles, body_angles):
+    ideal_elbow_range = (75, 90)
+    ideal_body_range = (0, 10)
+    elbow_score = sum(1 for angle in elbow_angles if ideal_elbow_range[0] <= angle <= ideal_elbow_range[1]) / len(elbow_angles) if elbow_angles else 0
+    body_score = sum(1 for angle in body_angles if ideal_body_range[0] <= angle <= ideal_body_range[1]) / len(body_angles) if body_angles else 0
+    return (elbow_score + body_score) / 2
+
+# Function to generate workout report
+def generate_workout_report(rep_scores, form_issues, analysis_time):
+    overall_efficiency = sum(rep_scores) / len(rep_scores) if rep_scores else 0
+    total_reps = len(rep_scores)
+    elbows_not_bending_enough = form_issues['elbows_not_bending_enough']
+    body_not_straight = form_issues['body_not_straight']
+
+    report = f"""
+    **Workout Report:**
+    -----------------
+    - **Total Push-ups:** {total_reps}
+    - **Overall Workout Efficiency:** {overall_efficiency * 100:.2f}%
+    - **Analysis Time:** {analysis_time:.2f} seconds
+
+    **Form Issues:**
+    - Elbows not bending enough: {elbows_not_bending_enough} reps ({(elbows_not_bending_enough / total_reps) * 100:.2f}% of reps)
+    - Body not straight: {body_not_straight} reps ({(body_not_straight / total_reps) * 100:.2f}% of reps)
+    """
+    return report
+
+# Load Lottie animation from a JSON file
+def load_lottiefile(filepath: str):
+    with open(filepath, "r") as f:
+        return json.load(f)
+
+# Streamlit app
+st.markdown("<h1 style='text-align: center;'>Push-Up Form Analysis</h1>", unsafe_allow_html=True)
+
+# Center the "Try Demo" button
+col1, col2, col3 = st.columns([1, 2, 1])
+with col2:
+    demo_button = st.button("Try Demo")
+
+# Path to the demo video
+demo_video_path = "W_58.mp4"
+
+# Video selection logic
+video_path = None
+if demo_button:
+    video_path = demo_video_path
+    st.success("Demo video loaded successfully!")
+
+st.write("Or upload your own video:")
+# File uploader for user's video
+uploaded_file = st.file_uploader("Choose a video file", type=["mp4", "mov", "avi"])
+if uploaded_file is not None:
+    with open("temp_video.mp4", "wb") as f:
+        f.write(uploaded_file.getvalue())
+    video_path = "temp_video.mp4"
+    st.success("Your video uploaded successfully!")
+
+if video_path:
+    cap = cv2.VideoCapture(video_path)
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    st.write(f"Frames per second: {fps}")
+
+    # Create placeholders for the three video outputs with titles
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        st.subheader("Original Video")
+        original_video = st.empty()
+    with col2:
+        st.subheader("Pose Points")
+        points_video = st.empty()
+    with col3:
+        st.subheader("Form Guide")
+        guide_video = st.empty()
+
+    feedback_placeholder = st.empty()
+
+    rep_scores = []
+    current_rep_angles = {'elbow': [], 'body': []}
+    form_issues = {
+        "elbows_not_bending_enough": 0,
+        "body_not_straight": 0
+    }
+    stage = "UP"
+    pushup_count = 0
+    start_time = time.time()
+
+    # Initialize form issues for the current rep
+    current_rep_issues = {
+        "elbows_not_bending_enough": False,
+        "body_not_straight": False
+    }
+
+    try:
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            image.flags.writeable = False
+            results = pose.process(image)
+            image.flags.writeable = True
+            image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+            if results.pose_landmarks:
+                try:
+                    landmarks = results.pose_landmarks.landmark
+                    shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x * width,
+                                landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y * height]
+                    elbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x * width,
+                             landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y * height]
+                    wrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x * width,
+                             landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y * height]
+                    hip = [landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x * width,
+                           landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y * height]
+                    ankle = [landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x * width,
+                             landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y * height]
+
+                    angle_elbow = calculate_angle(shoulder, elbow, wrist)
+                    angle_body = calculate_body_angle(shoulder, hip, ankle)
+
+                    current_rep_angles['elbow'].append(angle_elbow)
+                    current_rep_angles['body'].append(angle_body)
+
+                    points_image = np.zeros((height, width, 3), dtype=np.uint8)
+                    guide_image = np.zeros((height, width, 3), dtype=np.uint8)
+                    mp_drawing.draw_landmarks(points_image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
+                    mp_drawing.draw_landmarks(guide_image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
+
+                    if angle_elbow < 75:
+                        current_rep_issues["elbows_not_bending_enough"] = True
+                        cv2.line(guide_image, tuple(np.multiply(elbow, [1, 1]).astype(int)),
+                                 tuple(np.multiply(wrist, [1, 1]).astype(int)), (0, 255, 255), 5)  # Yellow for elbow issues
+                    if angle_body > 10:
+                        current_rep_issues["body_not_straight"] = True
+                        cv2.line(guide_image, tuple(np.multiply(shoulder, [1, 1]).astype(int)),
+                                 tuple(np.multiply(hip, [1, 1]).astype(int)), (0, 0, 255), 5)  # Red for body issues
+
+                    if angle_elbow > 90 and stage != "UP":
+                        stage = "UP"
+                        # Count issues for the completed rep
+                        for issue, occurred in current_rep_issues.items():
+                            if occurred:
+                                form_issues[issue] += 1
+                        # Reset current rep issues
+                        current_rep_issues = {k: False for k in current_rep_issues}
+
+                    if angle_elbow < 90 and stage == "UP":
+                        stage = "DOWN"
+                        pushup_count += 1
+                        rep_score = calculate_rep_score(current_rep_angles['elbow'], current_rep_angles['body'])
+                        rep_scores.append(rep_score)
+                        current_rep_angles = {'elbow': [], 'body': []}
+
+                    # Display the videos
+                    original_video.image(image, channels="BGR")
+                    points_video.image(points_image, channels="BGR")
+                    guide_video.image(guide_image, channels="BGR")
+                except AttributeError as e:
+                    st.error(f"Error processing frame: {e}")
+            else:
+                st.warning("No pose landmarks detected in this frame.")
+    except Exception as e:
+        st.error(f"Error occurred: {e}")
+    finally:
+        cap.release()
+
+    analysis_time = time.time() - start_time
+    report = generate_workout_report(rep_scores, form_issues, analysis_time)
+    feedback_placeholder.markdown(report)
+
+    #st.balloons()  # Add a fun animation when the analysis is done

pages/squat.py

@@ -0,0 +1,214 @@
+import streamlit as st
+import cv2
+import mediapipe as mp
+import numpy as np
+import time
+import json
+
+@st.cache_resource
+def load_pose_model():
+    mp_pose = mp.solutions.pose
+    pose = mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5)
+    return mp_pose, pose
+
+mp_pose, pose = load_pose_model()
+mp_drawing = mp.solutions.drawing_utils
+
+def calculate_angle(a, b, c):
+    a = np.array(a)  # First
+    b = np.array(b)  # Mid
+    c = np.array(c)  # End
+    radians = np.arctan2(c[1] - b[1], c[0] - b[0]) - np.arctan2(a[1] - b[1], a[0] - b[0])
+    angle = np.abs(radians * 180.0 / np.pi)
+    if angle > 180.0:
+        angle = 360 - angle
+    return angle
+
+def calculate_rep_score(knee_angles, hip_angles, back_angles):
+    ideal_knee_range = (90, 110)
+    ideal_hip_range = (80, 100)
+    ideal_back_range = (70, 90)
+    knee_score = sum(1 for angle in knee_angles if ideal_knee_range[0] <= angle <= ideal_knee_range[1]) / len(knee_angles) if knee_angles else 0
+    hip_score = sum(1 for angle in hip_angles if ideal_hip_range[0] <= angle <= ideal_hip_range[1]) / len(hip_angles) if hip_angles else 0
+    back_score = sum(1 for angle in back_angles if ideal_back_range[0] <= angle <= ideal_back_range[1]) / len(back_angles) if back_angles else 0
+    return (knee_score + hip_score + back_score) / 3
+
+def generate_workout_report(rep_scores, form_issues, analysis_time):
+    if rep_scores:
+        overall_efficiency = sum(rep_scores) / len(rep_scores)
+    else:
+        overall_efficiency = 0
+
+    total_reps = len(rep_scores)
+    
+    if total_reps > 0:
+        knee_percentage = form_issues['knees_bending_too_much'] / total_reps * 100
+        hip_percentage = form_issues['hips_bending_too_much'] / total_reps * 100
+        back_percentage = form_issues['back_leaning_too_much'] / total_reps * 100
+    else:
+        knee_percentage = 0
+        hip_percentage = 0
+        back_percentage = 0
+
+    report = f"""
+    Workout Report:
+    ---------------
+    Total Squats: {total_reps}
+    Overall Workout Efficiency: {overall_efficiency * 100:.2f}%
+    Analysis Time: {analysis_time:.2f} seconds
+    Form Issues:
+    - Knees bending too much: {form_issues['knees_bending_too_much']} reps ({knee_percentage:.2f}% of reps)
+    - Hips bending too much: {form_issues['hips_bending_too_much']} reps ({hip_percentage:.2f}% of reps)
+    - Back leaning too much: {form_issues['back_leaning_too_much']} reps ({back_percentage:.2f}% of reps)
+    """
+    return report
+
+def load_lottiefile(filepath: str):
+    with open(filepath, "r") as f:
+        return json.load(f)
+
+st.markdown("<h1 style='text-align: center;'>Squat Form Analysis</h1>", unsafe_allow_html=True)
+
+col1, col2, col3 = st.columns([1,2,1])
+with col2:
+    demo_button = st.button("Try Demo")
+
+demo_video_path = "demo.mp4"
+
+video_path = None
+if demo_button:
+    video_path = demo_video_path
+    st.success("Demo video loaded successfully!")
+
+st.write("Or upload your own video:")
+uploaded_file = st.file_uploader("Choose a video file", type=["mp4", "mov", "avi"])
+if uploaded_file is not None:
+    with open("temp_video.mp4", "wb") as f:
+        f.write(uploaded_file.getvalue())
+    video_path = "temp_video.mp4"
+    st.success("Your video uploaded successfully!")
+
+if video_path:
+    cap = cv2.VideoCapture(video_path)
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    st.write(f"Frames per second: {fps}")
+
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        st.subheader("Original Video")
+        original_video = st.empty()
+    with col2:
+        st.subheader("Pose Points")
+        points_video = st.empty()
+    with col3:
+        st.subheader("Form Guide")
+        guide_video = st.empty()
+
+    squat_count_placeholder = st.empty()
+    feedback_placeholder = st.empty()
+
+    rep_scores = []
+    current_rep_angles = {'knee': [], 'hip': [], 'back': []}
+    form_issues = {
+        "knees_bending_too_much": 0,
+        "hips_bending_too_much": 0,
+        "back_leaning_too_much": 0
+    }
+    stage = None
+    squat_count = 0
+    start_time = time.time()
+
+    current_rep_issues = {
+        "knees_bending_too_much": False,
+        "hips_bending_too_much": False,
+        "back_leaning_too_much": False
+    }
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        image.flags.writeable = False
+        results = pose.process(image)
+        image.flags.writeable = True
+        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+        if results.pose_landmarks is not None:
+            landmarks = results.pose_landmarks.landmark
+            shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x * width,
+                        landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y * height]
+            hip = [landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x * width,
+                   landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y * height]
+            knee = [landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x * width,
+                    landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y * height]
+            ankle = [landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x * width,
+                     landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y * height]
+
+            angle_knee = calculate_angle(hip, knee, ankle)
+            angle_hip = calculate_angle(shoulder, hip, knee)
+            angle_back = calculate_angle(shoulder, hip, ankle)
+
+            current_rep_angles['knee'].append(angle_knee)
+            current_rep_angles['hip'].append(angle_hip)
+            current_rep_angles['back'].append(angle_back)
+
+            points_image = np.zeros((height, width, 3), dtype=np.uint8)
+            guide_image = np.zeros((height, width, 3), dtype=np.uint8)
+            mp_drawing.draw_landmarks(points_image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
+
+            # Drawing on guide image with smaller markers
+            mp_drawing.draw_landmarks(
+                guide_image, 
+                results.pose_landmarks, 
+                mp_pose.POSE_CONNECTIONS,
+                mp_drawing.DrawingSpec(color=(0, 255, 0), thickness=1, circle_radius=2),  # Green for upper arm
+                mp_drawing.DrawingSpec(color=(255, 0, 0), thickness=1, circle_radius=2)  # Red for lower arm
+            )
+
+            if angle_knee < 90:
+                current_rep_issues["knees_bending_too_much"] = True
+                cv2.line(guide_image, tuple(np.multiply(knee, [1, 1]).astype(int)),
+                         tuple(np.multiply(ankle, [1, 1]).astype(int)), (0, 255, 255), 2)
+            if angle_hip < 80:
+                current_rep_issues["hips_bending_too_much"] = True
+                cv2.line(guide_image, tuple(np.multiply(hip, [1, 1]).astype(int)),
+                         tuple(np.multiply(knee, [1, 1]).astype(int)), (0, 0, 255), 2)
+            if angle_back < 70:
+                current_rep_issues["back_leaning_too_much"] = True
+                cv2.line(guide_image, tuple(np.multiply(shoulder, [1, 1]).astype(int)),
+                         tuple(np.multiply(hip, [1, 1]).astype(int)), (255, 0, 0), 2)
+
+            # Rep counting logic
+            if angle_knee > 160 and stage != "UP":
+                stage = "UP"
+                for issue, occurred in current_rep_issues.items():
+                    if occurred:
+                        form_issues[issue] += 1
+                rep_scores.append(calculate_rep_score(
+                    current_rep_angles['knee'],
+                    current_rep_angles['hip'],
+                    current_rep_angles['back']
+                ))
+                current_rep_angles = {'knee': [], 'hip': [], 'back': []}
+                current_rep_issues = {k: False for k in current_rep_issues}
+            elif angle_knee <= 90 and stage == "UP":
+                stage = "DOWN"
+                squat_count += 1
+                squat_count_placeholder.write(f"Squat Count: {squat_count}")
+
+        # Update video streams
+        original_video.image(frame, channels="BGR", use_column_width=True)
+        points_video.image(points_image, channels="BGR", use_column_width=True)
+        guide_video.image(guide_image, channels="BGR", use_column_width=True)
+
+    cap.release()
+    end_time = time.time()
+    analysis_time = end_time - start_time
+
+    # Generate and display the report
+    report = generate_workout_report(rep_scores, form_issues, analysis_time)
+    feedback_placeholder.text(report)

requirements.txt

@@ -0,0 +1,7 @@
+opencv-python-headless
+mediapipe
+numpy
+streamlit
+numpy 
+streamlit_webrtc
+streamlit-lottie