Create MLONNX-FIXED

MLONNX-FIXED

@@ -0,0 +1,651 @@
+import React, { useState, useEffect, useRef } from 'react';
+import { Mic, Square, Settings, Loader2, AlertCircle, Copy, CheckCircle2, ChevronDown, ChevronUp, Upload } from 'lucide-react';
+
+// --- Feature Extraction: Log-Mel Spectrogram ---
+// This model requires 80-dim log-mel spectrogram features, standard for Conformer models.
+const computeLogMelSpectrogram = (audioData) => {
+  const sr = 16000;
+  const n_fft = 512;
+  const win_length = 400; // 25ms
+  const hop_length = 160; // 10ms
+  const n_mels = 80;
+  const preemph = 0.97;
+
+  // 1. Preemphasis
+  const preemphasized = new Float32Array(audioData.length);
+  preemphasized[0] = audioData[0];
+  for (let i = 1; i < audioData.length; i++) {
+    preemphasized[i] = audioData[i] - preemph * audioData[i - 1];
+  }
+
+  // 2. Window (Hann)
+  const window = new Float32Array(win_length);
+  for (let i = 0; i < win_length; i++) {
+    window[i] = 0.5 - 0.5 * Math.cos((2 * Math.PI * i) / (win_length - 1));
+  }
+
+  // 3. Mel Filterbank
+  const fmin = 0;
+  const fmax = 8000;
+  const melMin = 2595 * Math.log10(1 + fmin / 700);
+  const melMax = 2595 * Math.log10(1 + fmax / 700);
+  const melPoints = Array.from({length: n_mels + 2}, (_, i) => melMin + i * (melMax - melMin) / (n_mels + 1));
+  const hzPoints = melPoints.map(m => 700 * (Math.pow(10, m / 2595) - 1));
+  const fftFreqs = Array.from({length: n_fft / 2 + 1}, (_, i) => (i * sr) / n_fft);
+  
+  const fbank = [];
+  for (let i = 0; i < n_mels; i++) {
+    const row = new Float32Array(n_fft / 2 + 1);
+    const f_left = hzPoints[i];
+    const f_center = hzPoints[i + 1];
+    const f_right = hzPoints[i + 2];
+    for (let j = 0; j < fftFreqs.length; j++) {
+      const f = fftFreqs[j];
+      if (f >= f_left && f <= f_center) {
+        row[j] = (f - f_left) / (f_center - f_left);
+      } else if (f >= f_center && f <= f_right) {
+        row[j] = (f_right - f) / (f_right - f_center);
+      }
+    }
+    fbank.push(row);
+  }
+
+  // 4. STFT & Log-Mel Computation
+  const numFrames = Math.floor((preemphasized.length - win_length) / hop_length) + 1;
+  if (numFrames <= 0) return { melSpec: new Float32Array(0), numFrames: 0 };
+  
+  const melSpec = new Float32Array(n_mels * numFrames);
+  
+  for (let frame = 0; frame < numFrames; frame++) {
+    const start = frame * hop_length;
+    const real = new Float32Array(n_fft);
+    const imag = new Float32Array(n_fft);
+    
+    for (let i = 0; i < win_length; i++) {
+      real[i] = preemphasized[start + i] * window[i];
+    }
+    
+    // Cooley-Tukey FFT
+    let j = 0;
+    for (let i = 0; i < n_fft - 1; i++) {
+      if (i < j) {
+        let tr = real[i]; real[i] = real[j]; real[j] = tr;
+        let ti = imag[i]; imag[i] = imag[j]; imag[j] = ti;
+      }
+      let m = n_fft >> 1;
+      while (m >= 1 && j >= m) { j -= m; m >>= 1; }
+      j += m;
+    }
+    
+    for (let l = 2; l <= n_fft; l <<= 1) {
+      let l2 = l >> 1;
+      let u1 = 1.0, u2 = 0.0;
+      let c1 = Math.cos(Math.PI / l2), c2 = -Math.sin(Math.PI / l2);
+      for (let j = 0; j < l2; j++) {
+        for (let i = j; i < n_fft; i += l) {
+          let i1 = i + l2;
+          let t1 = u1 * real[i1] - u2 * imag[i1];
+          let t2 = u1 * imag[i1] + u2 * real[i1];
+          real[i1] = real[i] - t1;
+          imag[i1] = imag[i] - t2;
+          real[i] += t1;
+          imag[i] += t2;
+        }
+        let z = u1 * c1 - u2 * c2;
+        u2 = u1 * c2 + u2 * c1;
+        u1 = z;
+      }
+    }
+
+    // Apply Mel Filterbank & Log
+    for (let m = 0; m < n_mels; m++) {
+      let melEnergy = 0;
+      for (let i = 0; i <= n_fft / 2; i++) {
+         const power = real[i] * real[i] + imag[i] * imag[i];
+         melEnergy += power * fbank[m][i];
+      }
+      const logMel = Math.log(Math.max(melEnergy, 1e-9));
+      melSpec[m * numFrames + frame] = logMel;
+    }
+  }
+
+  // 5. Feature Standardization (per-instance mean/var normalization)
+  for (let m = 0; m < n_mels; m++) {
+    let sum = 0;
+    for (let f = 0; f < numFrames; f++) {
+      sum += melSpec[m * numFrames + f];
+    }
+    const mean = sum / numFrames;
+    let sumSq = 0;
+    for (let f = 0; f < numFrames; f++) {
+      const diff = melSpec[m * numFrames + f] - mean;
+      sumSq += diff * diff;
+    }
+    const std = Math.sqrt(sumSq / numFrames) + 1e-9;
+    for (let f = 0; f < numFrames; f++) {
+      melSpec[m * numFrames + f] = (melSpec[m * numFrames + f] - mean) / std;
+    }
+  }
+
+  return { melSpec, numFrames };
+};
+
+
+export default function App() {
+  // App State
+  const [modelUrl, setModelUrl] = useState("https://huggingface.co/sulabhkatiyar/indicconformer-120m-onnx/resolve/main/ml/model.onnx");
+  const [vocabUrl, setVocabUrl] = useState("https://huggingface.co/sulabhkatiyar/indicconformer-120m-onnx/resolve/main/ml/vocab.json");
+  const [modelSource, setModelSource] = useState('url'); // 'url' | 'local'
+  const [localModel, setLocalModel] = useState(null);
+  const [localVocab, setLocalVocab] = useState(null);
+  
+  const [isOrtReady, setIsOrtReady] = useState(false);
+  const [session, setSession] = useState(null);
+  const [vocab, setVocab] = useState([]);
+  const [isLoading, setIsLoading] = useState(false);
+  
+  const [isRecording, setIsRecording] = useState(false);
+  const [status, setStatus] = useState("Please load the model to begin.");
+  const [transcript, setTranscript] = useState("");
+  const [copiedMessage, setCopiedMessage] = useState("");
+  
+  const [showSettings, setShowSettings] = useState(false);
+  const [errorMessage, setErrorMessage] = useState("");
+  
+  // Refs for Audio Recording
+  const mediaRecorderRef = useRef(null);
+  const audioChunksRef = useRef([]);
+  const fileInputRef = useRef(null);
+
+  // Load onnxruntime-web script dynamically
+  useEffect(() => {
+    if (window.ort) {
+      setIsOrtReady(true);
+      return;
+    }
+    const script = document.createElement('script');
+    script.src = "https://cdn.jsdelivr.net/npm/onnxruntime-web/dist/ort.min.js";
+    script.async = true;
+    script.onload = () => setIsOrtReady(true);
+    script.onerror = () => setErrorMessage("Failed to load onnxruntime-web library.");
+    document.body.appendChild(script);
+  }, []);
+
+  const loadVocabData = (text) => {
+    try {
+      const data = JSON.parse(text);
+      if (Array.isArray(data)) return data;
+      if (typeof data === 'object') {
+        const vocabArray = [];
+        for (const [token, index] of Object.entries(data)) {
+          vocabArray[index] = token;
+        }
+        return vocabArray;
+      }
+    } catch (e) {
+      return text.split('\n').map(line => line.trim()).filter(line => line.length > 0);
+    }
+    throw new Error("Invalid vocabulary format");
+  };
+
+  const initModel = async () => {
+    if (!isOrtReady || !window.ort) {
+      setErrorMessage("ONNX Runtime is not ready yet.");
+      return;
+    }
+    
+    setIsLoading(true);
+    setErrorMessage("");
+    setStatus("Downloading/Reading Vocabulary...");
+    
+    try {
+      let loadedVocab;
+      let sess;
+
+      if (modelSource === 'url') {
+        const res = await fetch(vocabUrl);
+        if (!res.ok) throw new Error(`Failed to load vocab from ${vocabUrl}`);
+        const text = await res.text();
+        loadedVocab = loadVocabData(text);
+        
+        setStatus("Downloading ONNX Model (100MB+). This may take a while...");
+        sess = await window.ort.InferenceSession.create(modelUrl, { 
+           executionProviders: ['wasm'] 
+        });
+      } else {
+        if (!localModel || !localVocab) {
+           throw new Error("Please select both the ONNX Model and Vocabulary files.");
+        }
+        const vocabText = await localVocab.text();
+        loadedVocab = loadVocabData(vocabText);
+
+        setStatus("Reading Local ONNX Model...");
+        const modelBuffer = await localModel.arrayBuffer();
+        sess = await window.ort.InferenceSession.create(new Uint8Array(modelBuffer), {
+           executionProviders: ['wasm']
+        });
+      }
+      
+      setVocab(loadedVocab);
+      setSession(sess);
+      setStatus("Model Loaded & Ready. Press the microphone to speak.");
+    } catch (err) {
+      console.error(err);
+      setErrorMessage(`Initialization Error: ${err.message}`);
+      setStatus("Failed to load model.");
+    } finally {
+      setIsLoading(false);
+    }
+  };
+
+  const startRecording = async () => {
+    try {
+      const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
+      const mediaRecorder = new MediaRecorder(stream);
+      audioChunksRef.current = [];
+      
+      mediaRecorder.ondataavailable = (e) => {
+        if (e.data.size > 0) audioChunksRef.current.push(e.data);
+      };
+      
+      mediaRecorder.onstop = processAndInfer;
+      mediaRecorderRef.current = mediaRecorder;
+      mediaRecorder.start();
+      
+      setIsRecording(true);
+      setStatus("Recording... Speak in Malayalam.");
+      setErrorMessage("");
+    } catch (err) {
+      console.error(err);
+      setErrorMessage("Microphone permission denied or an error occurred.");
+    }
+  };
+
+  const stopRecording = () => {
+    if (mediaRecorderRef.current && isRecording) {
+      mediaRecorderRef.current.stop();
+      setIsRecording(false);
+      // Stops all microphone tracks
+      mediaRecorderRef.current.stream.getTracks().forEach(track => track.stop());
+    }
+  };
+
+  const processAndInfer = async () => {
+    setStatus("Processing Audio...");
+    try {
+      // Decode audio and resample to 16kHz Mono Float32
+      const blob = new Blob(audioChunksRef.current);
+      const arrayBuffer = await blob.arrayBuffer();
+      const audioCtx = new (window.AudioContext || window.webkitAudioContext)({ sampleRate: 16000 });
+      const decodedData = await audioCtx.decodeAudioData(arrayBuffer);
+      const float32Data = decodedData.getChannelData(0); // Mono channel
+      
+      setStatus("Running Inference...");
+      await runInference(float32Data);
+    } catch (err) {
+      console.error(err);
+      setErrorMessage(`Audio Processing Error: ${err.message}`);
+      setStatus("Ready.");
+    }
+  };
+
+  const handleFileUpload = async (e) => {
+    const file = e.target.files[0];
+    if (!file) return;
+
+    setStatus("Processing Uploaded Audio...");
+    setErrorMessage("");
+    setIsLoading(true);
+    
+    try {
+      const arrayBuffer = await file.arrayBuffer();
+      const audioCtx = new (window.AudioContext || window.webkitAudioContext)({ sampleRate: 16000 });
+      const decodedData = await audioCtx.decodeAudioData(arrayBuffer);
+      const float32Data = decodedData.getChannelData(0); // Mono channel
+      
+      setStatus("Running Inference on File...");
+      await runInference(float32Data);
+    } catch (err) {
+      console.error(err);
+      setErrorMessage(`Audio Upload Error: ${err.message}`);
+      setStatus("Ready.");
+    } finally {
+      setIsLoading(false);
+      e.target.value = null; // Reset input to allow re-uploading the same file
+    }
+  };
+
+  const runInference = async (float32Data) => {
+    try {
+      const inputNames = session.inputNames;
+      const feeds = {};
+      
+      // Attempt 1: Raw Waveform tensor
+      if (inputNames.includes('audio_signal')) {
+        feeds['audio_signal'] = new window.ort.Tensor('float32', float32Data, [1, float32Data.length]);
+      } else {
+        throw new Error(`The model expects inputs: ${inputNames.join(', ')}.`);
+      }
+      
+      if (inputNames.includes('length')) {
+        feeds['length'] = new window.ort.Tensor('int64', new BigInt64Array([BigInt(float32Data.length)]), [1]);
+      }
+      
+      let results;
+      try {
+        results = await session.run(feeds);
+      } catch (runError) {
+        // Attempt 2: Feature-extracted Log-Mel Spectrogram (Catches "Expected: 3" or "Expected: 80" errors)
+        if (runError.message && (runError.message.includes("Expected: 3") || runError.message.includes("Expected: 80"))) {
+          console.warn("Raw audio tensor failed. Model likely lacks a feature extractor. Computing 80-bin Log-Mel Spectrogram natively...");
+          
+          const { melSpec, numFrames } = computeLogMelSpectrogram(float32Data);
+          if (numFrames <= 0) throw new Error("Audio sample is too short to process.");
+
+          feeds['audio_signal'] = new window.ort.Tensor('float32', melSpec, [1, 80, numFrames]);
+          
+          if (inputNames.includes('length')) {
+             feeds['length'] = new window.ort.Tensor('int64', new BigInt64Array([BigInt(numFrames)]), [1]);
+          }
+          
+          results = await session.run(feeds);
+        } else {
+          throw runError; // Unhandled error
+        }
+      }
+      
+      // Assume the first output contains the logprobs/logits
+      const outputName = session.outputNames[0]; 
+      const outputTensor = results[outputName];
+      const logits = outputTensor.data; 
+      let dims = outputTensor.dims;
+      
+      // Standardize dims to [batch, time, vocab]
+      if (dims.length === 2) dims = [1, dims[0], dims[1]];
+      
+      const text = decodeCTC(logits, dims, vocab);
+      setTranscript(prev => prev + (prev ? " " : "") + text);
+      setStatus("Transcription Complete. Ready for next.");
+    } catch (err) {
+      console.error(err);
+      setErrorMessage(`Inference Error: ${err.message}`);
+      setStatus("Ready.");
+    }
+  };
+
+  const decodeCTC = (logits, dims, vocabList) => {
+    const T = dims[1]; // Time frames
+    const V = dims[2]; // Vocab size emitted by model
+    let result = [];
+    let prev_id = -1;
+    
+    // In typical NeMo models, the blank token is the last index
+    const blankId = V - 1; 
+    
+    for (let t = 0; t < T; t++) {
+      let max_val = -Infinity;
+      let max_id = -1;
+      
+      for (let v = 0; v < V; v++) {
+        const val = logits[t * V + v];
+        if (val > max_val) {
+          max_val = val;
+          max_id = v;
+        }
+      }
+      
+      if (max_id !== prev_id && max_id !== blankId) {
+        let token = "";
+        if (max_id < vocabList.length) {
+           token = vocabList[max_id];
+        }
+        
+        // Ignore standard special tokens
+        if (token && token !== '<blank>' && token !== '<pad>' && token !== '<s>' && token !== '</s>') {
+           result.push(token);
+        }
+      }
+      prev_id = max_id;
+    }
+    
+    // Clean up SentencePiece artifacts (e.g., '_' or ' ') and Malayalam spacing character
+    let decodedText = result.join('');
+    decodedText = decodedText.replace(/ /g, ' ').replace(/_/g, ' ').replace(/▁/g, ' ').trim();
+    return decodedText.replace(/\s+/g, ' '); // Remove redundant spaces
+  };
+
+  const handleCopy = () => {
+    const textArea = document.createElement("textarea");
+    textArea.value = transcript;
+    document.body.appendChild(textArea);
+    textArea.select();
+    try {
+      document.execCommand('copy');
+      setCopiedMessage("Copied to clipboard!");
+      setTimeout(() => setCopiedMessage(""), 2000);
+    } catch (err) {
+      setCopiedMessage("Failed to copy");
+      setTimeout(() => setCopiedMessage(""), 2000);
+    }
+    document.body.removeChild(textArea);
+  };
+
+  return (
+    <div className="min-h-screen bg-neutral-50 dark:bg-neutral-900 text-neutral-900 dark:text-neutral-100 p-4 sm:p-8 font-sans selection:bg-blue-200 dark:selection:bg-blue-900">
+      <div className="max-w-3xl mx-auto space-y-6">
+        
+        {/* Header */}
+        <div className="text-center space-y-2">
+          <h1 className="text-3xl sm:text-4xl font-extrabold tracking-tight bg-clip-text text-transparent bg-gradient-to-r from-blue-600 to-indigo-600 dark:from-blue-400 dark:to-indigo-400">
+            Malayalam Speech-to-Text
+          </h1>
+          <p className="text-neutral-500 dark:text-neutral-400 text-sm sm:text-base">
+            Powered by IndicConformer-120M & ONNX Runtime Web
+          </p>
+        </div>
+
+        {/* Main Interface Card */}
+        <div className="bg-white dark:bg-neutral-800 rounded-2xl shadow-xl border border-neutral-100 dark:border-neutral-700 overflow-hidden">
+          
+          {/* Status Bar */}
+          <div className="bg-neutral-100 dark:bg-neutral-700/50 px-6 py-3 flex items-center justify-between">
+            <div className="flex items-center space-x-2 text-sm font-medium text-neutral-600 dark:text-neutral-300">
+              {isLoading ? (
+                <Loader2 size={16} className="animate-spin text-blue-500" />
+              ) : session ? (
+                <CheckCircle2 size={16} className="text-emerald-500" />
+              ) : (
+                <AlertCircle size={16} className="text-amber-500" />
+              )}
+              <span>{status}</span>
+            </div>
+            
+            <button 
+              onClick={() => setShowSettings(!showSettings)}
+              className="text-neutral-400 hover:text-neutral-600 dark:hover:text-neutral-200 transition-colors"
+              title="Settings"
+            >
+              <Settings size={18} />
+            </button>
+          </div>
+
+          {/* Settings Panel */}
+          {showSettings && (
+            <div className="px-6 py-4 bg-neutral-50 dark:bg-neutral-800/80 border-b border-neutral-100 dark:border-neutral-700 space-y-4">
+              <h3 className="text-sm font-semibold uppercase tracking-wider text-neutral-500 dark:text-neutral-400">
+                Model Configuration
+              </h3>
+              
+              <div className="flex items-center space-x-4 mb-2">
+                <label className="flex items-center space-x-2 text-sm text-neutral-700 dark:text-neutral-300">
+                  <input type="radio" value="url" checked={modelSource === 'url'} onChange={() => setModelSource('url')} className="text-blue-600 focus:ring-blue-500" />
+                  <span>Use URLs</span>
+                </label>
+                <label className="flex items-center space-x-2 text-sm text-neutral-700 dark:text-neutral-300">
+                  <input type="radio" value="local" checked={modelSource === 'local'} onChange={() => setModelSource('local')} className="text-blue-600 focus:ring-blue-500" />
+                  <span>Local Files</span>
+                </label>
+              </div>
+
+              <div className="space-y-3 text-sm">
+                {modelSource === 'url' ? (
+                  <>
+                    <div>
+                      <label className="block text-neutral-700 dark:text-neutral-300 mb-1 font-medium">ONNX Model URL</label>
+                      <input 
+                        type="text" 
+                        value={modelUrl}
+                        onChange={e => setModelUrl(e.target.value)}
+                        className="w-full p-2.5 border border-neutral-300 dark:border-neutral-600 rounded-lg bg-white dark:bg-neutral-900 focus:ring-2 focus:ring-blue-500 focus:border-blue-500 outline-none transition-all"
+                      />
+                    </div>
+                    <div>
+                      <label className="block text-neutral-700 dark:text-neutral-300 mb-1 font-medium">Vocabulary URL (.txt / .json)</label>
+                      <input 
+                        type="text" 
+                        value={vocabUrl}
+                        onChange={e => setVocabUrl(e.target.value)}
+                        className="w-full p-2.5 border border-neutral-300 dark:border-neutral-600 rounded-lg bg-white dark:bg-neutral-900 focus:ring-2 focus:ring-blue-500 focus:border-blue-500 outline-none transition-all"
+                      />
+                    </div>
+                  </>
+                ) : (
+                  <>
+                    <div>
+                      <label className="block text-neutral-700 dark:text-neutral-300 mb-1 font-medium">ONNX Model File (.onnx)</label>
+                      <input 
+                        type="file" 
+                        accept=".onnx"
+                        onChange={e => setLocalModel(e.target.files[0])}
+                        className="w-full text-neutral-700 dark:text-neutral-300 file:mr-4 file:py-2 file:px-4 file:rounded-lg file:border-0 file:text-sm file:font-semibold file:bg-blue-50 file:text-blue-700 hover:file:bg-blue-100 dark:file:bg-blue-900/30 dark:file:text-blue-400"
+                      />
+                    </div>
+                    <div>
+                      <label className="block text-neutral-700 dark:text-neutral-300 mb-1 font-medium">Vocabulary File (.json / .txt)</label>
+                      <input 
+                        type="file" 
+                        accept=".json,.txt"
+                        onChange={e => setLocalVocab(e.target.files[0])}
+                        className="w-full text-neutral-700 dark:text-neutral-300 file:mr-4 file:py-2 file:px-4 file:rounded-lg file:border-0 file:text-sm file:font-semibold file:bg-blue-50 file:text-blue-700 hover:file:bg-blue-100 dark:file:bg-blue-900/30 dark:file:text-blue-400"
+                      />
+                    </div>
+                  </>
+                )}
+                
+                <div className="flex items-center justify-between pt-2">
+                  <span className="text-xs text-neutral-500 dark:text-neutral-400 flex items-center">
+                    <AlertCircle size={12} className="inline mr-1" /> Re-initialize model after changing configuration.
+                  </span>
+                  <button
+                    onClick={initModel}
+                    disabled={isLoading}
+                    className="px-4 py-2 bg-neutral-200 dark:bg-neutral-700 hover:bg-neutral-300 dark:hover:bg-neutral-600 rounded-lg font-medium transition-colors text-sm"
+                  >
+                    Load / Refresh Model
+                  </button>
+                </div>
+              </div>
+            </div>
+          )}
+
+          {/* Action Area */}
+          <div className="p-8 flex flex-col items-center justify-center space-y-6">
+            
+            {/* Error Message Display */}
+            {errorMessage && (
+              <div className="w-full p-4 bg-red-50 dark:bg-red-900/20 text-red-600 dark:text-red-400 rounded-xl text-sm border border-red-100 dark:border-red-900/50 flex items-start">
+                <AlertCircle size={18} className="mr-2 flex-shrink-0 mt-0.5" />
+                <span>{errorMessage}</span>
+              </div>
+            )}
+
+            {!session && !isLoading && !errorMessage && (
+              <button
+                onClick={initModel}
+                className="px-8 py-4 bg-blue-600 hover:bg-blue-700 text-white rounded-xl font-bold shadow-lg hover:shadow-blue-600/30 transition-all transform hover:scale-105 active:scale-95"
+              >
+                Initialize Model
+              </button>
+            )}
+
+            {/* Input Controls */}
+            <div className="flex items-center space-x-6">
+              {/* Microphone Button */}
+              <button
+                onClick={isRecording ? stopRecording : startRecording}
+                disabled={!session || isLoading}
+                className={`p-8 rounded-full transition-all duration-300 group ${
+                  !session || isLoading 
+                    ? 'bg-neutral-200 dark:bg-neutral-800 text-neutral-400 dark:text-neutral-600 cursor-not-allowed'
+                    : isRecording 
+                      ? 'bg-red-500 hover:bg-red-600 animate-pulse text-white shadow-[0_0_40px_rgba(239,68,68,0.5)]' 
+                      : 'bg-blue-600 hover:bg-blue-700 text-white shadow-lg hover:shadow-[0_0_30px_rgba(37,99,235,0.4)] transform hover:scale-105 active:scale-95'
+                }`}
+                title="Record Audio"
+              >
+                {isRecording ? <Square size={40} className="fill-current" /> : <Mic size={40} />}
+              </button>
+
+              {/* Upload Button */}
+              <button
+                onClick={() => fileInputRef.current?.click()}
+                disabled={!session || isLoading || isRecording}
+                className={`p-8 rounded-full transition-all duration-300 group ${
+                  !session || isLoading || isRecording
+                    ? 'bg-neutral-200 dark:bg-neutral-800 text-neutral-400 dark:text-neutral-600 cursor-not-allowed'
+                    : 'bg-indigo-600 hover:bg-indigo-700 text-white shadow-lg hover:shadow-[0_0_30px_rgba(79,70,229,0.4)] transform hover:scale-105 active:scale-95'
+                }`}
+                title="Upload Audio File"
+              >
+                <Upload size={40} />
+              </button>
+              <input 
+                type="file" 
+                ref={fileInputRef} 
+                onChange={handleFileUpload} 
+                accept="audio/*" 
+                className="hidden" 
+              />
+            </div>
+            
+            <p className="text-neutral-500 dark:text-neutral-400 font-medium text-center">
+              {isRecording ? "Tap to Stop & Transcribe" : (session ? "Tap Mic to Record or Upload an Audio File" : "Model required to process audio")}
+            </p>
+          </div>
+
+          {/* Transcript Area */}
+          <div className="border-t border-neutral-100 dark:border-neutral-700 p-6 bg-neutral-50 dark:bg-neutral-800/50">
+            <div className="flex items-center justify-between mb-3">
+              <h3 className="font-semibold text-neutral-700 dark:text-neutral-300">Transcript</h3>
+              
+              {/* Copy Tools */}
+              <div className="flex items-center space-x-3">
+                {copiedMessage && <span className="text-xs text-green-500 font-medium animate-fade-in">{copiedMessage}</span>}
+                <button 
+                  onClick={handleCopy}
+                  disabled={!transcript}
+                  className="p-2 text-neutral-400 hover:text-blue-500 disabled:opacity-50 disabled:cursor-not-allowed transition-colors rounded-lg hover:bg-blue-50 dark:hover:bg-blue-900/20"
+                  title="Copy Transcript"
+                >
+                  <Copy size={18} />
+                </button>
+                <button 
+                  onClick={() => setTranscript("")}
+                  disabled={!transcript}
+                  className="text-xs font-medium px-3 py-1.5 rounded-lg text-neutral-500 hover:text-red-500 hover:bg-red-50 dark:hover:bg-red-900/20 transition-colors disabled:opacity-50 disabled:cursor-not-allowed"
+                >
+                  Clear
+                </button>
+              </div>
+            </div>
+            
+            <div className="w-full min-h-[120px] p-4 bg-white dark:bg-neutral-900 border border-neutral-200 dark:border-neutral-700 rounded-xl text-neutral-800 dark:text-neutral-200 font-medium text-lg leading-relaxed whitespace-pre-wrap break-words">
+              {transcript || <span className="text-neutral-400 dark:text-neutral-600 italic">Transcription will appear here...</span>}
+            </div>
+          </div>
+
+        </div>
+      </div>
+    </div>
+  );
+}