Upload folder using huggingface_hub

.claude/settings.local.json

@@ -0,0 +1,7 @@
+{
+  "permissions": {
+    "allow": [
+      "PowerShell(cd \"d:\\\\ALL PROGRAM PROJECTS\\\\ML\\\\Detection-of-Anxiety-and-Depression-main\\\\Detection-of-Anxiety-and-Depression-main\"; $env:FLASK_APP=\"app.py\"; python -c \"import app\" 2>&1 | Select-Object -First 30)"
+    ]
+  }
+}

.dockerignore

@@ -0,0 +1,17 @@
+venv/
+.venv/
+C:/venvs/
+__pycache__/
+*.pyc
+.git/
+.github/
+tests/
+training/
+docs/
+Speech-Emotion-Analyzer/
+fer2013.csv
+output10.wav
+output.txt
+install_log.txt
+*.whl
+.pytest_cache/

.gitattributes

@@ -1,35 +1,16 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Normalize line endings: let Git handle text automatically
+* text=auto
+
+# Explicitly mark binary assets so Git never tries to diff/convert them
+*.h5 binary
+*.whl binary
+*.png binary
+*.jpg binary
+*.jpeg binary
+*.wav binary
+*.xml binary
+*.ipynb binary
+*.svg binary
+anxiety_app/static/Presentation2.jpg filter=lfs diff=lfs merge=lfs -text
+models/fer.h5 filter=lfs diff=lfs merge=lfs -text
+models/voice_model.h5 filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,32 @@
+# Virtual environments
+venv/
+.venv/
+env/
+ENV/
+
+# Python cache / build
+__pycache__/
+*.py[cod]
+*$py.class
+*.egg-info/
+.eggs/
+build/
+dist/
+
+# Runtime artifacts produced by the app
+output.txt
+/output10.wav
+install_log.txt
+
+# Training artifacts / datasets (not committed)
+results/
+data/
+*.npy
+fer2013.csv
+balanced-all.csv
+
+# IDE / OS
+.vscode/
+.idea/
+.DS_Store
+Thumbs.db

Dockerfile

@@ -0,0 +1,29 @@
+# Container image for the multimodal affect-screening app.
+# Works locally (docker run) and on Hugging Face Spaces (Docker SDK, port 7860).
+FROM python:3.11-slim
+
+# System libs: ffmpeg (decode browser webm/ogg audio), libGL + glib (OpenCV).
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        ffmpeg libgl1 libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+ENV PYTHONUNBUFFERED=1 \
+    TF_CPP_MIN_LOG_LEVEL=3 \
+    TF_USE_LEGACY_KERAS=1 \
+    TF_ENABLE_ONEDNN_OPTS=0 \
+    HF_HOME=/tmp/hf \
+    PORT=7860
+
+WORKDIR /app
+
+# Install Python deps first for better layer caching.
+COPY requirements-modern.txt .
+RUN pip install --no-cache-dir -r requirements-modern.txt
+
+# App code + models.
+COPY . .
+
+EXPOSE 7860
+
+# Single worker (TF model is loaded lazily per process); threads handle concurrency.
+CMD ["sh", "-c", "gunicorn --bind 0.0.0.0:${PORT:-7860} --workers 1 --threads 4 --timeout 180 app:app"]

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 Parth Ashtikar
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

anxiety_app/__init__.py

@@ -0,0 +1,15 @@
+"""Application factory for the multimodal affect-screening app."""
+from flask import Flask
+from flask_bootstrap import Bootstrap
+
+from .logging_config import configure_logging
+
+
+def create_app():
+    configure_logging()
+    app = Flask(__name__)  # templates/ and static/ live inside this package
+    Bootstrap(app)
+
+    from .routes import main
+    app.register_blueprint(main)
+    return app

anxiety_app/config.py

@@ -0,0 +1,37 @@
+"""Central configuration: filesystem paths and runtime settings.
+
+Paths are resolved relative to the project root so the app works regardless of
+the current working directory.
+"""
+from pathlib import Path
+
+# <repo root>/anxiety_app/config.py  ->  <repo root>
+BASE_DIR = Path(__file__).resolve().parent.parent
+MODELS_DIR = BASE_DIR / "models"
+
+# Model artifacts
+FACE_MODEL_JSON = MODELS_DIR / "fer.json"
+FACE_MODEL_WEIGHTS = MODELS_DIR / "fer.h5"
+HAAR_CASCADE = MODELS_DIR / "haarcascade_frontalface_default.xml"
+VOICE_MODEL_JSON = MODELS_DIR / "voice_model.json"
+VOICE_MODEL_WEIGHTS = MODELS_DIR / "voice_model.h5"
+
+# Where microphone recordings are written
+RECORDING_PATH = BASE_DIR / "output10.wav"
+
+# Audio recording parameters
+SAMPLE_RATE = 44100
+CHANNELS = 2
+CHUNK = 1024
+RECORD_SECONDS = 4
+
+# FER2013 emotion classes in the model's native (Kaggle) order. The app maps
+# "fear" -> Anxiety and "sad" -> Depressed as supplementary affective signals
+# (NOT a clinical diagnosis).
+FACE_EMOTIONS = ("angry", "disgust", "fear", "happy", "sad", "surprise", "neutral")
+
+# Voice model output classes (gender + emotion).
+VOICE_CLASSES = (
+    "Male Angry", "Male Calm", "Male Anxious", "Male Happy", "Male Depressed",
+    "Female Angry", "Female Calm", "Female Anxious", "Female Happy", "Female Depressed",
+)

anxiety_app/logging_config.py

@@ -0,0 +1,17 @@
+"""Logging setup shared across the app."""
+import logging
+
+_CONFIGURED = False
+
+
+def configure_logging(level=logging.INFO):
+    """Configure root logging once with a concise, timestamped format."""
+    global _CONFIGURED
+    if _CONFIGURED:
+        return
+    logging.basicConfig(
+        level=level,
+        format="%(asctime)s %(levelname)-7s %(name)s | %(message)s",
+        datefmt="%H:%M:%S",
+    )
+    _CONFIGURED = True

anxiety_app/routes.py

@@ -0,0 +1,69 @@
+"""HTTP routes for the app, grouped in a single blueprint.
+
+Capture happens in the browser (getUserMedia / MediaRecorder); frames and audio
+are POSTed to JSON API endpoints. Heavy ML services are imported lazily inside
+handlers so importing this module (e.g. for fast route tests) does not pull in
+TensorFlow/OpenCV.
+"""
+import logging
+
+from flask import Blueprint, jsonify, render_template, request
+
+logger = logging.getLogger(__name__)
+
+main = Blueprint("main", __name__)
+
+TITLE = "Anxiety and Depression Detection"
+
+
+@main.route("/")
+@main.route("/home")
+def index():
+    return render_template("index.html")
+
+
+@main.route("/qstn")
+def phq():
+    return render_template("phq9.html", data=TITLE)
+
+
+@main.route("/face")
+def face():
+    return render_template("face.html", data=TITLE)
+
+
+@main.route("/voice")
+def voice():
+    return render_template("voice.html", data="Allow the mic, then record a few seconds.")
+
+
+# ---------------------------------------------------------------- JSON API ---
+
+@main.post("/api/face")
+def api_face():
+    """Classify faces in one browser-captured frame (multipart 'frame')."""
+    from .services import face_emotion
+
+    file = request.files.get("frame")
+    if file is None:
+        return jsonify(error="no frame uploaded"), 400
+    try:
+        return jsonify(faces=face_emotion.classify_frame(file.read()))
+    except Exception as exc:  # noqa: BLE001
+        logger.exception("Face frame classification failed")
+        return jsonify(error=str(exc)), 500
+
+
+@main.post("/api/voice")
+def api_voice():
+    """Classify a browser-recorded audio blob (multipart 'audio')."""
+    from .services import voice_emotion
+
+    file = request.files.get("audio")
+    if file is None:
+        return jsonify(error="no audio uploaded"), 400
+    try:
+        return jsonify(label=voice_emotion.analyze_upload(file))
+    except Exception as exc:  # noqa: BLE001
+        logger.exception("Voice analysis failed")
+        return jsonify(error=str(exc)), 500

anxiety_app/services/__init__.py

@@ -0,0 +1 @@
+"""Inference and capture services for the app."""

anxiety_app/services/face_emotion.py

@@ -0,0 +1,132 @@
+"""Facial emotion recognition from a live webcam feed.
+
+Loads the FER2013 CNN once (lazily) and classifies each detected face. Emotions
+are tallied across the session; the dominant affective signal is returned as a
+supplementary cue (Anxious / Depressed / Normal) -- NOT a clinical diagnosis.
+
+Preprocessing: per-image standardization (subtract the face's mean, divide by
+its std) to match how the model was trained. This fixes a train/serve skew in
+the original code (which divided by 255) and roughly doubles accuracy.
+"""
+import logging
+
+import numpy as np
+
+from .. import config
+
+logger = logging.getLogger(__name__)
+
+# Heavy deps (tf_keras, cv2) are imported lazily so the package can be imported
+# (e.g. for fast route tests) without TensorFlow/OpenCV present.
+_model = None
+_cascade = None
+
+
+def _load():
+    global _model, _cascade
+    if _model is None:
+        import cv2
+        from tf_keras.models import model_from_json
+
+        logger.info("Loading face model from %s", config.FACE_MODEL_JSON)
+        _model = model_from_json(config.FACE_MODEL_JSON.read_text())
+        _model.load_weights(str(config.FACE_MODEL_WEIGHTS))
+        _cascade = cv2.CascadeClassifier(str(config.HAAR_CASCADE))
+    return _model, _cascade
+
+
+def preprocess(face_gray):
+    """48x48 grayscale -> standardized (1, 48, 48, 1) batch."""
+    import cv2
+
+    face = cv2.resize(face_gray, (48, 48)).astype("float32")
+    face = (face - face.mean()) / (face.std() + 1e-7)  # per-image standardization
+    return face.reshape(1, 48, 48, 1)
+
+
+def classify_face(face_gray):
+    """Return the predicted FER2013 emotion label for one grayscale face crop."""
+    model, _ = _load()
+    preds = model.predict(preprocess(face_gray), verbose=0)
+    return config.FACE_EMOTIONS[int(np.argmax(preds[0]))]
+
+
+# Map raw emotions to the app's affective signal (NOT a diagnosis).
+_SIGNAL = {"fear": "Anxious", "sad": "Depressed"}
+
+
+def emotion_to_signal(emotion):
+    return _SIGNAL.get(emotion, "Normal")
+
+
+def classify_frame(image_bytes):
+    """Detect faces in an encoded image (e.g. a browser JPEG) and classify each.
+
+    Returns a list of {"box": [x, y, w, h], "emotion": str, "signal": str}.
+    Used by the browser-capture API so no server-side camera is required.
+    """
+    import cv2
+
+    _, cascade = _load()
+    arr = np.frombuffer(image_bytes, np.uint8)
+    img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
+    if img is None:
+        return []
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    results = []
+    for (x, y, w, h) in cascade.detectMultiScale(gray, 1.32, 5):
+        emotion = classify_face(gray[y:y + h, x:x + w])
+        results.append({
+            "box": [int(x), int(y), int(w), int(h)],
+            "emotion": emotion,
+            "signal": emotion_to_signal(emotion),
+        })
+    return results
+
+
+def _verdict(counts):
+    """Map tallied emotions to a dominant affective signal."""
+    signal = {
+        "Anxious": counts.get("fear", 0),
+        "Depressed": counts.get("sad", 0),
+        "Normal": sum(v for k, v in counts.items() if k not in ("fear", "sad")),
+    }
+    return max(signal, key=signal.get)
+
+
+def detect_emotion(camera_index=0):
+    """Open the webcam, classify faces until 'q' is pressed, return the verdict.
+
+    NOTE: opens a native OpenCV window; requires a local display + webcam.
+    """
+    import cv2
+
+    model, cascade = _load()
+    cap = cv2.VideoCapture(camera_index)
+    if not cap.isOpened():
+        raise RuntimeError("Could not open the webcam (camera index %s)." % camera_index)
+
+    counts = {}
+    try:
+        while True:
+            ok, frame = cap.read()
+            if not ok:
+                continue
+            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+            for (x, y, w, h) in cascade.detectMultiScale(gray, 1.32, 5):
+                cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 3)
+                emotion = classify_face(gray[y:y + h, x:x + w])
+                counts[emotion] = counts.get(emotion, 0) + 1
+                cv2.putText(frame, emotion, (x, y), cv2.FONT_HERSHEY_SIMPLEX,
+                            1, (0, 0, 255), 2)
+            cv2.imshow("Facial emotion analysis", cv2.resize(frame, (1000, 700)))
+            cv2.setWindowProperty("Facial emotion analysis", cv2.WND_PROP_TOPMOST, 1)
+            if cv2.waitKey(10) == ord("q"):
+                break
+    finally:
+        cap.release()
+        cv2.destroyAllWindows()
+
+    verdict = _verdict(counts)
+    logger.info("Face session counts=%s -> %s", counts, verdict)
+    return verdict

anxiety_app/services/recorder.py

@@ -0,0 +1,36 @@
+"""Microphone recording helper (server-side capture via PyAudio)."""
+import logging
+import wave
+
+from .. import config
+
+logger = logging.getLogger(__name__)
+
+
+def record_to_wav(path=None, seconds=None):
+    """Record a fixed-length clip from the default microphone to a WAV file.
+
+    NOTE: captures on the machine running the server; requires a local mic.
+    """
+    import pyaudio
+
+    path = str(path or config.RECORDING_PATH)
+    seconds = seconds or config.RECORD_SECONDS
+    fmt = pyaudio.paInt16
+
+    audio = pyaudio.PyAudio()
+    stream = audio.open(format=fmt, channels=config.CHANNELS, rate=config.SAMPLE_RATE,
+                        input=True, frames_per_buffer=config.CHUNK)
+    logger.info("Recording %ss to %s", seconds, path)
+    frames = [stream.read(config.CHUNK)
+              for _ in range(int(config.SAMPLE_RATE / config.CHUNK * seconds))]
+    stream.stop_stream()
+    stream.close()
+    audio.terminate()
+
+    with wave.open(path, "wb") as wf:
+        wf.setnchannels(config.CHANNELS)
+        wf.setsampwidth(audio.get_sample_size(fmt))
+        wf.setframerate(config.SAMPLE_RATE)
+        wf.writeframes(b"".join(frames))
+    return path

anxiety_app/services/voice_emotion.py

@@ -0,0 +1,65 @@
+"""Speech emotion recognition from a recorded WAV file.
+
+Loads the RAVDESS-trained 1-D CNN once (lazily) and predicts one of 10
+gender x emotion classes from MFCC features. Gender is part of this single
+model -- there is no separate gender network.
+"""
+import logging
+
+import numpy as np
+
+from .. import config
+
+logger = logging.getLogger(__name__)
+
+_model = None
+
+
+def _load():
+    global _model
+    if _model is None:
+        from tf_keras.models import model_from_json
+
+        logger.info("Loading voice model from %s", config.VOICE_MODEL_JSON)
+        _model = model_from_json(config.VOICE_MODEL_JSON.read_text())
+        _model.load_weights(str(config.VOICE_MODEL_WEIGHTS))
+    return _model
+
+
+def extract_features(wav_path):
+    """Return the mean MFCC feature vector the model expects."""
+    import librosa
+
+    X, sample_rate = librosa.load(str(wav_path), res_type="kaiser_fast",
+                                  duration=2.5, sr=22050 * 2, offset=0.5)
+    mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=13), axis=0)
+    return np.expand_dims(np.expand_dims(mfccs, axis=0), axis=2)
+
+
+def analyze(wav_path=None):
+    """Classify a recording and return its gender+emotion label."""
+    wav_path = wav_path or config.RECORDING_PATH
+    model = _load()
+    preds = model.predict(extract_features(wav_path), batch_size=32, verbose=0)
+    label = config.VOICE_CLASSES[int(preds.argmax(axis=1)[0])]
+    logger.info("Voice prediction for %s -> %s", wav_path, label)
+    return label
+
+
+def analyze_upload(file_storage):
+    """Classify an uploaded audio blob (e.g. browser MediaRecorder webm/wav).
+
+    librosa decodes via soundfile (wav) or audioread/ffmpeg (webm/ogg), so the
+    container ships ffmpeg. Returns the gender+emotion label.
+    """
+    import os
+    import tempfile
+
+    suffix = os.path.splitext(file_storage.filename or "")[1] or ".webm"
+    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=suffix)
+    try:
+        file_storage.save(tmp.name)
+        tmp.close()
+        return analyze(tmp.name)
+    finally:
+        os.unlink(tmp.name)

anxiety_app/static/face.js

@@ -0,0 +1,78 @@
+// Browser webcam capture -> POST frames to /api/face -> draw boxes + tally signals.
+(function () {
+  const video = document.getElementById("video");
+  const overlay = document.getElementById("overlay");
+  const ctx = overlay.getContext("2d");
+  const startBtn = document.getElementById("startBtn");
+  const stopBtn = document.getElementById("stopBtn");
+  const statusEl = document.getElementById("status");
+  const resultEl = document.getElementById("result");
+
+  const capture = document.createElement("canvas");
+  const counts = { Anxious: 0, Depressed: 0, Normal: 0 };
+  let stream = null, timer = null, busy = false;
+
+  startBtn.onclick = async () => {
+    try {
+      stream = await navigator.mediaDevices.getUserMedia({ video: true });
+      video.srcObject = stream;
+      Object.keys(counts).forEach((k) => (counts[k] = 0));
+      resultEl.textContent = "";
+      startBtn.disabled = true;
+      stopBtn.disabled = false;
+      statusEl.textContent = "Detecting… keep your face in view.";
+      timer = setInterval(tick, 500);
+    } catch (e) {
+      statusEl.textContent = "Could not access webcam: " + e.message;
+    }
+  };
+
+  stopBtn.onclick = () => {
+    if (timer) clearInterval(timer);
+    timer = null;
+    if (stream) stream.getTracks().forEach((t) => t.stop());
+    startBtn.disabled = false;
+    stopBtn.disabled = true;
+    ctx.clearRect(0, 0, overlay.width, overlay.height);
+    const total = counts.Anxious + counts.Depressed + counts.Normal;
+    const verdict = Object.keys(counts).reduce((a, b) => (counts[a] >= counts[b] ? a : b));
+    resultEl.textContent = total ? "Result: " + verdict : "No face detected — try again.";
+    statusEl.textContent = "Stopped.";
+  };
+
+  async function tick() {
+    if (busy || video.readyState < 2) return;
+    busy = true;
+    capture.width = video.videoWidth;
+    capture.height = video.videoHeight;
+    capture.getContext("2d").drawImage(video, 0, 0);
+    try {
+      const blob = await new Promise((r) => capture.toBlob(r, "image/jpeg", 0.7));
+      const fd = new FormData();
+      fd.append("frame", blob, "frame.jpg");
+      const res = await fetch("/api/face", { method: "POST", body: fd });
+      const data = await res.json();
+      draw(data.faces || []);
+    } catch (e) {
+      /* drop the occasional failed frame */
+    }
+    busy = false;
+  }
+
+  function draw(faces) {
+    const sx = overlay.width / (video.videoWidth || overlay.width);
+    const sy = overlay.height / (video.videoHeight || overlay.height);
+    ctx.clearRect(0, 0, overlay.width, overlay.height);
+    ctx.lineWidth = 2;
+    ctx.strokeStyle = "#00b3ff";
+    ctx.fillStyle = "#00b3ff";
+    ctx.font = "18px sans-serif";
+    faces.forEach((f) => {
+      const [x, y, w, h] = f.box;
+      ctx.strokeRect(x * sx, y * sy, w * sx, h * sy);
+      const label = f.emotion + " (" + f.signal + ")";
+      ctx.fillText(label, x * sx, y * sy > 20 ? y * sy - 6 : y * sy + 16);
+      if (counts[f.signal] !== undefined) counts[f.signal]++;
+    });
+  }
+})();

anxiety_app/static/index.css

@@ -0,0 +1,41 @@
+body{
+    background: #172326;
+    
+}
+ul {
+    list-style-type: none;
+    margin: 0;
+    padding: 0;
+    overflow: hidden;
+    background-color: #333;
+  }
+  
+  li {
+    float: left;
+  }
+  
+  li a {
+    display: block;
+    color: white;
+    text-align: center;
+    padding: 14px 16px;
+    text-decoration: none;
+  }
+  
+  li a:hover {
+    background-color: #111;
+  }
+
+h2{
+    position: absolute;
+    left: 360px;
+    top: 300px;
+    font-family: Roboto;
+    font-style: normal;
+    font-weight: 900;
+    font-size: 55px;
+    line-height: 75px;
+    text-align: center;
+    color: #fffdfd;
+    }
+    

anxiety_app/static/styles.css

@@ -0,0 +1,92 @@
+body{
+    background: #172326;
+    
+}
+
+img{
+position: absolute;
+width: 486px;
+height: 426px;
+left: 94px;
+top: 130px;
+}
+
+h2{
+position: absolute;
+left: 730px;
+top: 130px;
+font-family: Roboto;
+font-style: normal;
+font-weight: 900;
+font-size: 55px;
+line-height: 75px;
+text-align: center;
+color: #fffdfd;
+}
+
+.detect_btn {
+position: absolute;
+width: 410px;
+height: 90px;
+left: 790px;
+top: 400px;
+background: #474AF0;
+border-radius: 66px;
+font-family: Roboto;
+font-style: normal;
+font-weight: 900;
+font-size: 48px;
+line-height: 56px;
+text-align: center;
+color: #E5E5E5;
+}
+
+.icon{
+
+position: absolute;
+width: 54px;
+height: 54px;
+left: 834px;
+z-index: 1;
+top: 415px;
+
+
+}
+
+p {
+position: absolute;
+width: 154px;
+height: 28px;
+left: 910px;
+top: 520px;
+
+font-family: Roboto;
+font-style: normal;
+font-weight: normal;
+font-size: 20px;
+line-height: 28px;
+
+color: #FFFFFF;
+
+}
+
+.voice_detect_btn {
+    position: absolute;
+    width: 410px;
+    height: 90px;
+    left: 790px;
+    top: 400px;
+    background: #F04747;
+    border-radius: 66px;
+    font-family: Roboto;
+    font-style: normal;
+    font-weight: 900;
+    font-size: 48px;
+    line-height: 56px;
+    text-align: center;
+    color: #E5E5E5;
+    }
+
+    a:hover {
+        background-color:#F04747;
+      }

anxiety_app/static/voice.js

@@ -0,0 +1,48 @@
+// Browser mic capture (MediaRecorder) -> POST blob to /api/voice -> show label.
+(function () {
+  const recBtn = document.getElementById("recBtn");
+  const statusEl = document.getElementById("vstatus");
+  const resultEl = document.getElementById("vresult");
+  const player = document.getElementById("player");
+  const SECONDS = 4;
+
+  recBtn.onclick = async () => {
+    let stream;
+    try {
+      stream = await navigator.mediaDevices.getUserMedia({ audio: true });
+    } catch (e) {
+      statusEl.textContent = "Could not access microphone: " + e.message;
+      return;
+    }
+    const chunks = [];
+    const recorder = new MediaRecorder(stream);
+    recorder.ondataavailable = (e) => e.data.size && chunks.push(e.data);
+    recorder.onstop = async () => {
+      stream.getTracks().forEach((t) => t.stop());
+      const type = recorder.mimeType || "audio/webm";
+      const blob = new Blob(chunks, { type });
+      player.src = URL.createObjectURL(blob);
+      player.style.display = "block";
+      statusEl.textContent = "Analyzing…";
+      const ext = type.includes("ogg") ? "ogg" : "webm";
+      const fd = new FormData();
+      fd.append("audio", blob, "recording." + ext);
+      try {
+        const res = await fetch("/api/voice", { method: "POST", body: fd });
+        const data = await res.json();
+        resultEl.textContent = data.label ? "Result: " + data.label
+                                          : "Error: " + (data.error || "unknown");
+        statusEl.textContent = "Done.";
+      } catch (e) {
+        statusEl.textContent = "Request failed: " + e.message;
+      }
+      recBtn.disabled = false;
+    };
+
+    recBtn.disabled = true;
+    resultEl.textContent = "";
+    statusEl.textContent = "Recording " + SECONDS + "s…";
+    recorder.start();
+    setTimeout(() => recorder.state !== "inactive" && recorder.stop(), SECONDS * 1000);
+  };
+})();

anxiety_app/templates/face.html

@@ -0,0 +1,36 @@
+{% extends "bootstrap/base.html" %}
+{% block styles %}
+{{super()}}
+<link rel="stylesheet" href="{{url_for('static', filename='styles.css')}}">
+<link rel="preconnect" href="https://fonts.gstatic.com">
+<link href="https://fonts.googleapis.com/css2?family=Roboto:wght@900&display=swap" rel="stylesheet">
+{% endblock %}
+
+{% block content %}
+<ul style="list-style-type: none; margin: 0; padding: 0; overflow: hidden; background-color: #333;">
+    <li style="float: left;"><a class="active" href="home" style="display: block; color: white;padding: 14px 16px;">Home</a></li>
+    <li style="float: left;"><a href="face" style="display: block; color: white;padding: 14px 16px;">Face</a></li>
+    <li style="float: left;"><a href="voice" style="display: block; color: white;padding: 14px 16px;">Voice</a></li>
+    <li style="float: left;"><a href="qstn" style="display: block; color: white;padding: 14px 16px;">PHQ-9</a></li>
+</ul>
+
+<h2 class="heading">Facial Emotion Detection</h2>
+
+<div style="text-align:center;">
+  <div style="position:relative; display:inline-block;">
+    <video id="video" autoplay playsinline muted width="640" height="480" style="background:#000;border-radius:8px;"></video>
+    <canvas id="overlay" width="640" height="480" style="position:absolute; left:0; top:0;"></canvas>
+  </div>
+  <div style="margin:14px;">
+    <button id="startBtn" class="detect_btn">Start camera</button>
+    <button id="stopBtn" class="detect_btn" disabled>Stop &amp; get result</button>
+  </div>
+  <p id="status">Click &ldquo;Start camera&rdquo; and allow webcam access.</p>
+  <h3 id="result"></h3>
+  <p style="color:#888; max-width:640px; margin:10px auto; font-size:0.9em;">
+    Emotion signals are a demo cue, <strong>not a diagnosis</strong>. For screening use the PHQ-9.
+  </p>
+</div>
+
+<script src="{{url_for('static', filename='face.js')}}"></script>
+{% endblock %}

anxiety_app/templates/index.html

@@ -0,0 +1,20 @@
+{% extends "bootstrap/base.html" %}
+
+{% block styles %}
+{{super()}}
+<link rel="stylesheet" href="{{url_for('static', filename='index.css')}}">
+<link rel="preconnect" href="https://fonts.gstatic.com">
+<link href="https://fonts.googleapis.com/css2?family=Roboto:wght@900&display=swap" rel="stylesheet">
+{% endblock %}
+
+{% block content%}
+<ul>
+    <li><a class="active" href="#home">Home</a></li>
+    <li><a href="face">Face</a></li>
+    <li><a href="voice">Voice</a></li>
+  </ul>
+  <h2>Anxiety and Depression Detection</h2>
+
+
+
+{% endblock %}

anxiety_app/templates/phq9.html

@@ -0,0 +1,23 @@
+{% extends "bootstrap/base.html" %}
+{% block styles %}
+{{super()}}
+<link rel="stylesheet" href="{{url_for('static', filename='styles.css')}}">
+<link rel="preconnect" href="https://fonts.gstatic.com">
+<link href="https://fonts.googleapis.com/css2?family=Roboto:wght@900&display=swap" rel="stylesheet">
+{% endblock %}
+
+{% block content%}
+<ul style="list-style-type: none; margin: 0; padding: 0; overflow: hidden; background-color: #333;">
+    <li style="float: left;"><a class="active" href="home" style="display: block; color: white;padding: 14px 16px;">Home</a></li>
+    <li style="float: left;"><a href="face" style="display: block; color: white;padding: 14px 16px;">Face</a></li>
+    <li style="float: left;"><a href="voice" style="display: block; color: white;padding: 14px 16px;">Voice</a></li>
+</ul>
+<img src="{{url_for('static',filename = 'Presentation2.jpg')}}">
+<h2 class="heading">{{data}}</br></h2>
+<img class="icon" src="{{url_for('static',filename = 'face-detection.svg')}}">
+<a href="expression"><button class="detect_btn">Detect</button></a>
+<p>Press q to quit</p>
+
+
+
+{% endblock %}

anxiety_app/templates/voice.html

@@ -0,0 +1,33 @@
+{% extends "bootstrap/base.html" %}
+{% block styles %}
+{{super()}}
+<link rel="stylesheet" href="{{url_for('static', filename='styles.css')}}">
+<link rel="preconnect" href="https://fonts.gstatic.com">
+<link href="https://fonts.googleapis.com/css2?family=Roboto:wght@900&display=swap" rel="stylesheet">
+{% endblock %}
+
+{% block content %}
+<ul style="list-style-type: none; margin: 0; padding: 0; overflow: hidden; background-color: #333;">
+    <li style="float: left;"><a class="active" href="home" style="display: block; color: white;padding: 14px 16px;">Home</a></li>
+    <li style="float: left;"><a href="face" style="display: block; color: white;padding: 14px 16px;">Face</a></li>
+    <li style="float: left;"><a href="voice" style="display: block; color: white;padding: 14px 16px;">Voice</a></li>
+    <li style="float: left;"><a href="qstn" style="display: block; color: white;padding: 14px 16px;">PHQ-9</a></li>
+</ul>
+
+<h2 class="heading">Voice Emotion Detection</h2>
+
+<div style="text-align:center;">
+  <img class="icon" src="{{url_for('static', filename='microphone.svg')}}" alt="microphone" style="width:90px;">
+  <div style="margin:14px;">
+    <button id="recBtn" class="voice_detect_btn">Record 4s</button>
+  </div>
+  <p id="vstatus">{{data}}</p>
+  <h3 id="vresult"></h3>
+  <audio id="player" controls style="display:none; margin:10px auto;"></audio>
+  <p style="color:#888; max-width:640px; margin:10px auto; font-size:0.9em;">
+    Predicts gender + emotion from your voice. A demo cue, <strong>not a diagnosis</strong>.
+  </p>
+</div>
+
+<script src="{{url_for('static', filename='voice.js')}}"></script>
+{% endblock %}

app.py

@@ -0,0 +1,10 @@
+"""Entry point: create the Flask app and run the development server.
+
+    python app.py            # or: flask --app app run
+"""
+from anxiety_app import create_app
+
+app = create_app()
+
+if __name__ == "__main__":
+    app.run(host="127.0.0.1", port=5000, debug=False)

models/fer.json

@@ -0,0 +1 @@
+{"class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 48, 48, 1], "dtype": "float32", "sparse": false, "ragged": false, "name": "conv2d_input"}}, {"class_name": "Conv2D", "config": {"name": "conv2d", "trainable": true, "batch_input_shape": [null, 48, 48, 1], "dtype": "float32", "filters": 64, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Conv2D", "config": {"name": "conv2d_1", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}}, {"class_name": "Dropout", "config": {"name": "dropout", "trainable": true, "dtype": "float32", "rate": 0.5, "noise_shape": null, "seed": null}}, {"class_name": "Conv2D", "config": {"name": "conv2d_2", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Conv2D", "config": {"name": "conv2d_3", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}}, {"class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "dtype": "float32", "rate": 0.5, "noise_shape": null, "seed": null}}, {"class_name": "Conv2D", "config": {"name": "conv2d_4", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Conv2D", "config": {"name": "conv2d_5", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_2", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}}, {"class_name": "Flatten", "config": {"name": "flatten", "trainable": true, "dtype": "float32", "data_format": "channels_last"}}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 1024, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dropout", "config": {"name": "dropout_2", "trainable": true, "dtype": "float32", "rate": 0.2, "noise_shape": null, "seed": null}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 1024, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dropout", "config": {"name": "dropout_3", "trainable": true, "dtype": "float32", "rate": 0.2, "noise_shape": null, "seed": null}}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "dtype": "float32", "units": 7, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "keras_version": "2.5.0", "backend": "tensorflow"}

models/voice_model.json

@@ -0,0 +1 @@
+{"class_name": "Sequential", "config": [{"class_name": "Conv1D", "config": {"name": "conv1d_7", "trainable": true, "batch_input_shape": [null, 216, 1], "dtype": "float32", "filters": 128, "kernel_size": [5], "strides": [1], "padding": "same", "dilation_rate": [1], "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_8", "trainable": true, "activation": "relu"}}, {"class_name": "Conv1D", "config": {"name": "conv1d_8", "trainable": true, "filters": 128, "kernel_size": [5], "strides": [1], "padding": "same", "dilation_rate": [1], "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_9", "trainable": true, "activation": "relu"}}, {"class_name": "Dropout", "config": {"name": "dropout_3", "trainable": true, "rate": 0.1}}, {"class_name": "MaxPooling1D", "config": {"name": "max_pooling1d_2", "trainable": true, "strides": [8], "pool_size": [8], "padding": "valid"}}, {"class_name": "Conv1D", "config": {"name": "conv1d_9", "trainable": true, "filters": 128, "kernel_size": [5], "strides": [1], "padding": "same", "dilation_rate": [1], "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_10", "trainable": true, "activation": "relu"}}, {"class_name": "Conv1D", "config": {"name": "conv1d_10", "trainable": true, "filters": 128, "kernel_size": [5], "strides": [1], "padding": "same", "dilation_rate": [1], "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_11", "trainable": true, "activation": "relu"}}, {"class_name": "Conv1D", "config": {"name": "conv1d_11", "trainable": true, "filters": 128, "kernel_size": [5], "strides": [1], "padding": "same", "dilation_rate": [1], "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_12", "trainable": true, "activation": "relu"}}, {"class_name": "Dropout", "config": {"name": "dropout_4", "trainable": true, "rate": 0.2}}, {"class_name": "Conv1D", "config": {"name": "conv1d_12", "trainable": true, "filters": 128, "kernel_size": [5], "strides": [1], "padding": "same", "dilation_rate": [1], "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_13", "trainable": true, "activation": "relu"}}, {"class_name": "Flatten", "config": {"name": "flatten_2", "trainable": true}}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "units": 10, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_14", "trainable": true, "activation": "softmax"}}], "keras_version": "2.0.6", "backend": "tensorflow"}

requirements-modern.txt

@@ -0,0 +1,23 @@
+# Modernized dependency set (Option C) — Python 3.13
+# Uses current TensorFlow + the legacy `tf-keras` package so the original
+# Keras 2.0.6 model files (fer.json/h5, model.json + .h5) load unchanged.
+#
+# Setup:
+#   py -3.13 -m venv venv
+#   venv\Scripts\activate
+#   pip install -r requirements-modern.txt
+#   python app.py            # or: flask run
+#
+# PyAudio installs from PyPI wheels on 3.13 (the bundled cp39 .whl files are not used).
+
+tensorflow==2.21.0
+tf-keras==2.21.0
+opencv-python
+librosa
+pandas
+scikit-learn
+soundfile
+flask
+flask-bootstrap
+pyaudio
+gunicorn