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<!DOCTYPE html>
<html lang="en" data-theme="light">
<head>
  <meta charset="UTF-8" />
  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
  <title>SpeakFlow AI – Speech Stuttering Detection</title>

  <!-- Font Awesome -->
  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.5.0/css/all.min.css" />

  <!-- Google Fonts -->
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  <link rel="stylesheet" href="style.css" />
</head>
<body>

  <!-- ===== NAVBAR ===== -->
  <nav class="navbar" id="navbar">
    <div class="nav-container">
      <div class="nav-logo">
        <i class="fas fa-wave-square"></i>
        <span>SpeakFlow <span class="logo-ai">AI</span></span>
      </div>
      <ul class="nav-links" id="navLinks">
        <li><a href="#overview">Overview</a></li>
        <li><a href="#analysis">Analysis</a></li>
        <li><a href="#recorder">Recorder</a></li>
        <li><a href="#results">Results</a></li>
        <li><a href="#history">History</a></li>
        <li><a href="#how-it-works">How It Works</a></li>
      </ul>
      <div class="nav-actions">
        <div class="nav-user-info" id="navUserInfo" style="display:none;">
          <button class="btn-user-profile" id="navProfileBtn">
            <i class="fas fa-user-circle"></i>
            <span id="navUserName">User</span>
          </button>
          <button class="btn-logout" id="navLogoutBtn" title="Logout">
            <i class="fas fa-sign-out-alt"></i> Logout
          </button>
        </div>
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        </button>
        <button class="hamburger" id="hamburger">
          <i class="fas fa-bars"></i>
        </button>
      </div>
    </div>
  </nav>

  <!-- ===== HERO SECTION ===== -->
  <section class="hero" id="hero">
    <div class="particles" id="particles"></div>
    <div class="hero-container">
      <div class="hero-content">
        <div class="hero-badge">
          <i class="fas fa-brain"></i> AI-Powered Healthcare
        </div>
        <h1 class="hero-title">
          AI-Powered Speech<br>
          <span class="gradient-text">Stuttering Detection</span>
        </h1>
        <p class="hero-subtitle">
          Upload your voice and receive intelligent speech fluency analysis using Machine Learning — powered by MFCC feature extraction and Random Forest classification.
        </p>
        <div class="hero-buttons">
          <a href="#analysis" class="btn btn-primary">
            <i class="fas fa-upload"></i> Upload Audio
          </a>
          <a href="#recorder" class="btn btn-outline">
            <i class="fas fa-microphone"></i> Try Live Demo
          </a>
        </div>
        <div class="hero-stats">
          <div class="hero-stat"><span>8,000+</span><label>Audio Samples</label></div>
          <div class="hero-stat"><span>40</span><label>MFCC Features</label></div>
          <div class="hero-stat"><span>RF</span><label>Classifier</label></div>
          <div class="hero-stat"><span>High</span><label>Accuracy</label></div>
        </div>
      </div>
      <div class="hero-visual">
        <div class="ai-sphere">
          <div class="sphere-ring ring-1"></div>
          <div class="sphere-ring ring-2"></div>
          <div class="sphere-ring ring-3"></div>
          <div class="sphere-core">
            <i class="fas fa-wave-square"></i>
          </div>
        </div>
        <div class="waveform-container">
          <canvas id="heroWaveform" width="400" height="80"></canvas>
        </div>
      </div>
    </div>
  </section>

  <!-- ===== PROJECT OVERVIEW ===== -->
  <section class="section" id="overview">
    <div class="container">
      <div class="section-header">
        <span class="section-eyebrow">Project Pipeline</span>
        <h2 class="section-title">How SpeakFlow AI Works</h2>
        <p class="section-desc">A complete ML pipeline from raw audio to intelligent prediction.</p>
      </div>
      <div class="overview-grid">
        <div class="overview-card">
          <div class="card-icon icon-blue"><i class="fas fa-database"></i></div>
          <h3>Audio Collection</h3>
          <p>8,000+ labeled audio samples of normal and stuttered speech collected from diverse speakers to build a robust training dataset.</p>
          <div class="card-tag">Dataset</div>
        </div>
        <div class="overview-card">
          <div class="card-icon icon-teal"><i class="fas fa-broom"></i></div>
          <h3>Audio Cleaning</h3>
          <p>Background noise removal, normalization, and silence trimming ensure clean signals before feature extraction begins.</p>
          <div class="card-tag">Preprocessing</div>
        </div>
        <div class="overview-card">
          <div class="card-icon icon-purple"><i class="fas fa-chart-bar"></i></div>
          <h3>MFCC Extraction</h3>
          <p>Mel-Frequency Cepstral Coefficients (40 features) are extracted using Librosa — the gold standard for speech analysis.</p>
          <div class="card-tag">Feature Engineering</div>
        </div>
        <div class="overview-card">
          <div class="card-icon icon-green"><i class="fas fa-tree"></i></div>
          <h3>Random Forest</h3>
          <p>An ensemble of decision trees classifies speech patterns, providing both prediction and probability estimates for each class.</p>
          <div class="card-tag">ML Model</div>
        </div>
        <div class="overview-card">
          <div class="card-icon icon-orange"><i class="fas fa-bullseye"></i></div>
          <h3>Prediction System</h3>
          <p>The final output delivers binary classification (Normal / Stutter) along with a confidence score and fluency rating.</p>
          <div class="card-tag">Inference</div>
        </div>
      </div>
    </div>
  </section>

  <!-- ===== AUDIO ANALYSIS SECTION ===== -->
  <section class="section section-alt" id="analysis">
    <div class="container">
      <div class="section-header">
        <span class="section-eyebrow">Audio Analysis</span>
        <h2 class="section-title">Upload & Analyze Speech</h2>
        <p class="section-desc">Upload a WAV or MP3 file to run the stuttering detection model.</p>
      </div>
      <div class="analysis-layout">
        <div class="upload-panel">
          <!-- Drag & Drop Zone -->
          <div class="drop-zone" id="dropZone">
            <div class="drop-icon"><i class="fas fa-cloud-upload-alt"></i></div>
            <h3>Drag & Drop Audio File</h3>
            <p>Supports WAV and MP3 formats</p>
            <button class="btn btn-primary btn-sm" id="browseBtn">
              <i class="fas fa-folder-open"></i> Browse File
            </button>
            <input type="file" id="fileInput" accept=".wav,.mp3,audio/wav,audio/mpeg" hidden />
          </div>

          <!-- File Preview -->
          <div class="file-preview" id="filePreview" style="display:none;">
            <div class="file-info">
              <div class="file-icon"><i class="fas fa-file-audio"></i></div>
              <div class="file-details">
                <span class="file-name" id="fileName"></span>
                <div class="file-meta">
                  <span id="fileSize"></span>
                  <span>·</span>
                  <span id="fileDuration"></span>
                </div>
              </div>
              <button class="file-remove" id="clearFileBtn" title="Remove file">
                <i class="fas fa-times"></i>
              </button>
            </div>
            <audio id="audioPlayer" controls></audio>
            <div class="action-buttons">
              <button class="btn btn-primary" id="analyzeBtn">
                <i class="fas fa-brain"></i> Analyze Speech
              </button>
              <button class="btn btn-ghost" id="clearBtn">
                <i class="fas fa-trash"></i> Clear
              </button>
            </div>
          </div>
        </div>

        <div class="upload-info">
          <h3><i class="fas fa-info-circle"></i> Analysis Details</h3>
          <ul class="info-list">
            <li><i class="fas fa-check-circle"></i> MFCC features extracted (40 coefficients)</li>
            <li><i class="fas fa-check-circle"></i> Random Forest ensemble inference</li>
            <li><i class="fas fa-check-circle"></i> Confidence score provided</li>
            <li><i class="fas fa-check-circle"></i> Speech fluency rating calculated</li>
            <li><i class="fas fa-check-circle"></i> Result saved to history</li>
            <li><i class="fas fa-check-circle"></i> PDF report available</li>
          </ul>
          <div class="format-chips">
            <span class="chip chip-blue"><i class="fas fa-file-audio"></i> WAV</span>
            <span class="chip chip-teal"><i class="fas fa-file-audio"></i> MP3</span>
          </div>
        </div>
      </div>
    </div>
  </section>

  <!-- ===== LOADING SCREEN ===== -->
  <div class="loading-overlay" id="loadingOverlay" style="display:none;">
    <div class="loading-card">
      <div class="loading-sphere">
        <i class="fas fa-brain"></i>
      </div>
      <h3>Analyzing Speech...</h3>
      <p>Running AI pipeline on your audio file</p>
      <div class="progress-bar-wrap">
        <div class="progress-bar" id="progressBar"></div>
      </div>
      <div class="progress-pct" id="progressPct">0%</div>
      <div class="loading-steps" id="loadingSteps">
        <div class="step" id="step1"><i class="fas fa-circle-notch fa-spin"></i> Uploading Audio</div>
        <div class="step" id="step2"><i class="fas fa-circle"></i> Cleaning Audio</div>
        <div class="step" id="step3"><i class="fas fa-circle"></i> Extracting MFCC Features</div>
        <div class="step" id="step4"><i class="fas fa-circle"></i> Running Random Forest Model</div>
        <div class="step" id="step5"><i class="fas fa-circle"></i> Generating Prediction</div>
      </div>
    </div>
  </div>

  <!-- ===== LIVE VOICE RECORDER ===== -->
  <section class="section" id="recorder">
    <div class="container">
      <div class="section-header">
        <span class="section-eyebrow">Live Recording</span>
        <h2 class="section-title">Record Your Voice</h2>
        <p class="section-desc">Use your microphone to record speech and analyze it directly.</p>
      </div>
      <div class="recorder-card">
        <div class="recorder-top">
          <div class="timer-display" id="timerDisplay">00:00</div>
          <div class="rec-indicator" id="recIndicator"></div>
        </div>
        <canvas id="liveWaveform" width="600" height="80"></canvas>
        <div class="recorder-controls">
          <button class="rec-btn btn-start" id="startRecBtn">
            <i class="fas fa-microphone"></i> Start Recording
          </button>
          <button class="rec-btn btn-stop" id="stopRecBtn" disabled>
            <i class="fas fa-stop"></i> Stop
          </button>
          <button class="rec-btn btn-play" id="playRecBtn" disabled>
            <i class="fas fa-play"></i> Play
          </button>
          <button class="rec-btn btn-delete" id="deleteRecBtn" disabled>
            <i class="fas fa-trash"></i> Delete
          </button>
        </div>
        <audio id="recPlayer" style="display:none;"></audio>
        <button class="btn btn-primary mt-16" id="analyzeRecBtn" disabled style="display:none;">
          <i class="fas fa-brain"></i> Analyze Recording
        </button>
      </div>
    </div>
  </section>

  <!-- ===== RESULTS DASHBOARD ===== -->
  <section class="section section-alt" id="results">
    <div class="container">
      <div class="section-header">
        <span class="section-eyebrow">AI Output</span>
        <h2 class="section-title">Results Dashboard</h2>
        <p class="section-desc">Your speech fluency analysis results appear here after processing.</p>
      </div>

      <!-- Placeholder when no results -->
      <div class="results-placeholder" id="resultsPlaceholder">
        <i class="fas fa-chart-pie"></i>
        <p>No analysis run yet. Upload or record audio above to get results.</p>
      </div>

      <!-- Results content (hidden until analysis) -->
      <div class="results-content" id="resultsContent" style="display:none;">
        <div class="results-grid">
          <!-- Prediction Card -->
          <div class="prediction-card" id="predictionCard">
            <div class="pred-icon" id="predIcon"></div>
            <div class="pred-label" id="predLabel"></div>
            <div class="pred-sublabel" id="predSublabel"></div>
          </div>

          <!-- Score Cards -->
          <div class="score-cards">
            <div class="score-card">
              <div class="score-value" id="confidenceScore"></div>
              <div class="score-label">Confidence Score</div>
              <i class="fas fa-shield-alt score-icon"></i>
            </div>
            <div class="score-card">
              <div class="score-value" id="fluencyScore"></div>
              <div class="score-label">Fluency Score</div>
              <i class="fas fa-wave-square score-icon"></i>
            </div>
            <div class="score-card">
              <div class="score-value" id="processingTime"></div>
              <div class="score-label">Processing Time</div>
              <i class="fas fa-clock score-icon"></i>
            </div>
            <div class="score-card">
              <div class="score-value" id="predProbability"></div>
              <div class="score-label">Pred. Probability</div>
              <i class="fas fa-percentage score-icon"></i>
            </div>
          </div>

          <!-- Charts -->
          <div class="charts-row">
            <div class="chart-card">
              <h4><i class="fas fa-chart-pie"></i> Probability Distribution</h4>
              <canvas id="pieChart" width="260" height="260"></canvas>
            </div>
            <div class="chart-card">
              <h4><i class="fas fa-chart-bar"></i> Feature Importance</h4>
              <canvas id="barChart" width="340" height="260"></canvas>
            </div>
            <div class="chart-card">
              <h4><i class="fas fa-tachometer-alt"></i> Confidence Meter</h4>
              <canvas id="gaugeChart" width="260" height="260"></canvas>
            </div>
            <div class="chart-card">
              <h4><i class="fas fa-star"></i> Speech Quality</h4>
              <canvas id="qualityChart" width="260" height="260"></canvas>
            </div>
          </div>

          <!-- Animated Waveform Result -->
          <div class="waveform-result-card">
            <h4><i class="fas fa-wave-square"></i> Analyzed Waveform</h4>
            <canvas id="resultWaveform" width="800" height="80"></canvas>
          </div>
        </div>

        <!-- Download Report -->
        <div class="report-section">
          <button class="btn btn-primary btn-lg" id="downloadReportBtn">
            <i class="fas fa-file-pdf"></i> Download PDF Report
          </button>
          <button class="btn btn-outline" id="newAnalysisBtn">
            <i class="fas fa-redo"></i> New Analysis
          </button>
        </div>
      </div>
    </div>
  </section>

  <!-- ===== DETECTION HISTORY ===== -->
  <section class="section" id="history">
    <div class="container">
      <div class="section-header">
        <span class="section-eyebrow">Detection History</span>
        <h2 class="section-title">Past Analyses</h2>
        <p class="section-desc">All previous analyses stored locally on your device.</p>
      </div>
      <div class="history-controls">
        <div class="search-box">
          <i class="fas fa-search"></i>
          <input type="text" id="historySearch" placeholder="Search by filename or result..." />
        </div>
        <button class="btn btn-danger btn-sm" id="clearAllBtn">
          <i class="fas fa-trash"></i> Clear All
        </button>
      </div>
      <div class="history-table-wrap">
        <table class="history-table" id="historyTable">
          <thead>
            <tr>
              <th>#</th>
              <th>Date & Time</th>
              <th>File Name</th>
              <th>Result</th>
              <th>Confidence</th>
              <th>Action</th>
            </tr>
          </thead>
          <tbody id="historyBody">
            <!-- Rows populated by JS -->
          </tbody>
        </table>
        <div class="history-empty" id="historyEmpty">
          <i class="fas fa-history"></i>
          <p>No history yet. Run an analysis to see results here.</p>
        </div>
      </div>
    </div>
  </section>

  <!-- ===== HOW AI WORKS ===== -->
  <section class="section section-alt" id="how-it-works">
    <div class="container">
      <div class="section-header">
        <span class="section-eyebrow">AI Pipeline</span>
        <h2 class="section-title">How the AI Works</h2>
        <p class="section-desc">A step-by-step walkthrough of the machine learning pipeline.</p>
      </div>
      <div class="timeline">
        <div class="timeline-item">
          <div class="timeline-dot dot-blue"><i class="fas fa-upload"></i></div>
          <div class="timeline-content">
            <div class="timeline-step">Step 1</div>
            <h3>Audio Upload</h3>
            <p>User uploads a WAV or MP3 audio file. The file is validated for format, size, and duration before being passed to the preprocessing pipeline.</p>
          </div>
        </div>
        <div class="timeline-item">
          <div class="timeline-dot dot-teal"><i class="fas fa-broom"></i></div>
          <div class="timeline-content">
            <div class="timeline-step">Step 2</div>
            <h3>Audio Preprocessing</h3>
            <p>The audio signal is cleaned — background noise is reduced using spectral subtraction, amplitude is normalized, and silences are trimmed to isolate speech.</p>
          </div>
        </div>
        <div class="timeline-item">
          <div class="timeline-dot dot-purple"><i class="fas fa-chart-bar"></i></div>
          <div class="timeline-content">
            <div class="timeline-step">Step 3</div>
            <h3>MFCC Feature Extraction</h3>
            <p>Librosa extracts 40 Mel-Frequency Cepstral Coefficients from the processed signal. These features encode the tonal characteristics of speech, capturing rhythm disruptions indicative of stuttering.</p>
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            <div class="timeline-step">Step 4</div>
            <h3>Random Forest Classification</h3>
            <p>The 40-feature vector is fed into a trained Random Forest ensemble. Each tree votes on the classification; the majority determines the predicted class and probability score.</p>
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            <div class="timeline-step">Step 5</div>
            <h3>Prediction Result</h3>
            <p>The final prediction — Normal or Stuttering — is returned with a confidence score, fluency rating, and processing time. The result is stored in history and available as a PDF report.</p>
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