added something

PRANA/.gitignore

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+mediapipe_env
+*.log
+
+# Ignore node_modules folder
+node_modules/
+
+# Ignore all .env files (environment variables)
+.env
+
+# Ignore a specific file
+secret_config.json
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+sample_venv/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintainted in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

PRANA/main.py

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+from flask import Flask, render_template, Response, request
+import cv2
+import mediapipe as mp
+import numpy as np  # For angle calculations
+
+
+app = Flask(__name__)
+
+mp_pose = mp.solutions.pose
+pose = mp_pose.Pose(static_image_mode=False, min_detection_confidence=0.6, model_complexity=1)
+mp_drawing = mp.solutions.drawing_utils
+
+
+def detectPose(image, pose, display=False):
+    output_image = image.copy()
+    imageRGB = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    results = pose.process(imageRGB)
+    height, width, _ = image.shape
+    landmarks = []
+
+    if results.pose_landmarks:
+        mp_drawing.draw_landmarks(output_image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
+        for landmark in results.pose_landmarks.landmark:
+            landmarks.append((int(landmark.x * width), int(landmark.y * height), (landmark.z * width)))
+
+    return output_image, landmarks
+
+
+def calculateAngle(a, b, c):
+    a = np.array(a)
+    b = np.array(b)
+    c = np.array(c)
+
+    ba = a - b
+    bc = c - b
+
+    cosine_angle = np.dot(ba, bc) / (np.linalg.norm(ba) * np.linalg.norm(bc))
+    angle = np.arccos(np.clip(cosine_angle, -1.0, 1.0))
+    return np.degrees(angle)
+
+
+def classifyPose(landmarks, output_image, selected_asana, display=False):
+    label = "Unknown Pose"
+    color = (0, 0, 255)
+
+    if not landmarks or len(landmarks) < 33:
+        cv2.putText(output_image, label, (10, 30), cv2.FONT_HERSHEY_PLAIN, 2, color, 2)
+        return output_image, label
+
+    # Extract key joint angles
+    left_elbow_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value], 
+                                      landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value], 
+                                      landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value])
+
+    right_elbow_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value], 
+                                       landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value], 
+                                       landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value])
+
+    left_shoulder_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value], 
+                                         landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value], 
+                                         landmarks[mp_pose.PoseLandmark.LEFT_HIP.value])
+
+    right_shoulder_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value], 
+                                          landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value], 
+                                          landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value])
+
+    left_knee_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.LEFT_HIP.value], 
+                                     landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value], 
+                                     landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value])
+
+    right_knee_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value], 
+                                      landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value], 
+                                      landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value])
+
+    hip_distance = np.linalg.norm(np.array(landmarks[mp_pose.PoseLandmark.LEFT_HIP.value][:2]) - 
+                                  np.array(landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value][:2])) 
+
+    # Check only the selected asana
+    if selected_asana == "Trikonasana":
+        if (165 < left_knee_angle < 195 and 165 < right_knee_angle < 195 and 
+            130 < left_elbow_angle < 180 and 175 < right_elbow_angle < 220):
+            label = "Trikonasana"
+
+    elif selected_asana == "Virabadrasana":
+        if (90 < left_knee_angle < 120 and 165 < right_knee_angle < 195):
+            label = "Virabadrasana"
+
+    elif selected_asana == "Vrikshasana":
+        if (315 < left_knee_angle < 335 or 25 < right_knee_angle < 45):
+            label = "Vrikshasana"
+
+    elif selected_asana == "Bhujangasana":
+        if (160 < left_elbow_angle < 190 and 160 < right_elbow_angle < 190 and 
+            50 < left_shoulder_angle < 80 and 50 < right_shoulder_angle < 80):
+            label = "Bhujangasana"
+
+    elif selected_asana == "Sukhasana":
+        if (40 < left_knee_angle < 70 and 40 < right_knee_angle < 70 and 
+            60 < left_shoulder_angle < 90 and 60 < right_shoulder_angle < 90 and hip_distance < 150):
+            label = "Sukhasana"
+
+    elif selected_asana == "Chakrasana":
+        if (240 < left_elbow_angle < 280 and 240 < right_elbow_angle < 280 and
+            250 < left_knee_angle < 290 and 250 < right_knee_angle < 290):
+            label = "Chakrasana"
+
+    # If the correct pose is detected, turn the label green
+    if label == selected_asana:
+        color = (0, 255, 0)  # Green if pose matches
+    else:
+        label = "Unknown Pose"
+
+    cv2.putText(output_image, label, (10, 30), cv2.FONT_HERSHEY_PLAIN, 2, color, 2)
+    return output_image, label
+
+
+def webcam_feed(selected_asana):
+    camera_video = cv2.VideoCapture(0)
+    camera_video.set(3, 1380)
+    camera_video.set(4, 960)
+
+    while camera_video.isOpened():
+        ok, frame = camera_video.read()
+        if not ok:
+            continue
+
+        frame = cv2.flip(frame, 1)
+        frame_height, frame_width, _ = frame.shape
+        frame = cv2.resize(frame, (int(frame_width * (640 / frame_height)), 640))
+
+        frame, landmarks = detectPose(frame, pose, display=False)
+
+        if landmarks:
+            frame, detected_asana = classifyPose(landmarks, frame, selected_asana, display=False)
+
+            # Display Accuracy
+            if detected_asana == selected_asana:
+                accuracy_text = f"{detected_asana}, 100%"
+            else:
+                accuracy_text = "Unknown Pose, 0%"
+
+            cv2.putText(frame, accuracy_text, (20, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+
+        ret, jpeg = cv2.imencode('.jpg', frame)
+        frame = jpeg.tobytes()
+
+        yield (b'--frame\r\n'
+               b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
+
+    camera_video.release()
+    cv2.destroyAllWindows()
+
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/yoga_try')
+def yoga_try():
+    asana = request.args.get('asana', 'Unknown')
+    return render_template('yoga_try.html', asana=asana)
+
+@app.route('/video_feed1')
+def video_feed1():
+    asana = request.args.get('asana', 'Unknown')
+    return Response(webcam_feed(asana), mimetype='multipart/x-mixed-replace; boundary=frame')
+
+if __name__ == '__main__':
+    app.run(debug=True)

PRANA/models/Message.js

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+const mongoose = require('mongoose');
+const messageSchema = new mongoose.Schema({
+  from: { type: mongoose.Schema.Types.ObjectId, ref: 'User', required: true },  // Sender
+  to: { type: mongoose.Schema.Types.ObjectId, ref: 'User', required: true },    // Receiver
+  text: { type: String, required: true },                                       // Message content
+  timestamp: { type: Date, default: Date.now },                                 // Time when the message was sent
+});
+
+const Message = mongoose.model('Message', messageSchema);
+
+module.exports = Message;

PRANA/models/User.js

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+const mongoose = require('mongoose');
+
+const userSchema = new mongoose.Schema({
+    username: { type: String, unique: true, required: true },
+    email: { type: String, unique: true, required: true },
+    password: { type: String, required: true },  // Password will be hashed
+    friends: [{
+        userId: { type: mongoose.Schema.Types.ObjectId, ref: 'User'},
+        lastChatTime: { type: Date, default: Date.now }
+    }] // References to other users they are chatting with
+});
+
+const User = mongoose.model('User', userSchema);
+
+module.exports = User;