app.py

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)
""")