add app.py & requirements.txt

app.py

@@ -0,0 +1,157 @@
+import streamlit as st
+import streamlit.components.v1 as components
+import tempfile
+import base64
+import os
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+from torch.nn.functional import softmax
+import torch
+
+# Load Whisper (fine-tuned zh_tw)
+whisper_model = pipeline("automatic-speech-recognition", model="Jingmiao/whisper-small-zh_tw")
+
+# Intent classifier models
+available_models = {
+    "ALBERT-tiny (Chinese)": "Luigi/albert-tiny-chinese-dinercall-intent",
+    "ALBERT-base (Chinese)": "Luigi/albert-base-chinese-dinercall-intent",
+}
+
+@st.cache_resource
+def load_model(model_id):
+    tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
+    model = AutoModelForSequenceClassification.from_pretrained(model_id)
+    return tokenizer, model
+
+def predict_intent(text, model_id):
+    tokenizer, model = load_model(model_id)
+    inputs = tokenizer(text, return_tensors="pt")
+    with torch.no_grad():
+        logits = model(**inputs).logits
+        probs = softmax(logits, dim=-1)
+        confidence = probs[0, 1].item()
+    if confidence >= 0.7:
+        label = "📞 訂位意圖 (Reservation intent)"
+    else:
+        label = "❌ 無訂位意圖 (No intent)"
+    return f"{label}（信心度 Confidence: {confidence:.2%}）"
+
+# UI
+st.title("🍽️ 餐廳訂位意圖識別")
+st.markdown("錄音或輸入文字，自動判斷是否具有訂位意圖。")
+
+model_label = st.selectbox("選擇模型", list(available_models.keys()))
+model_id = available_models[model_label]
+
+# JS-based mic recorder
+st.markdown("### 🎙️ 點擊錄音（支援瀏覽器）")
+components.html("""
+  <script>
+    let mediaRecorder;
+    let audioChunks = [];
+    let stream;
+
+    async function startRecording() {
+      stream = await navigator.mediaDevices.getUserMedia({ audio: true });
+      mediaRecorder = new MediaRecorder(stream);
+      mediaRecorder.ondataavailable = e => {
+        audioChunks.push(e.data);
+      };
+      mediaRecorder.onstop = e => {
+        const audioBlob = new Blob(audioChunks, { type: 'audio/webm' });
+        audioChunks = [];
+        const reader = new FileReader();
+        reader.onloadend = () => {
+          const base64Audio = reader.result.split(',')[1];
+          const streamlitEvent = new CustomEvent("streamlit:recordedAudio", {
+            detail: base64Audio
+          });
+          window.dispatchEvent(streamlitEvent);
+        };
+        reader.readAsDataURL(audioBlob);
+      };
+      mediaRecorder.start();
+      document.getElementById("status").innerText = "🎙️ 錄音中... 按下停止結束錄音";
+    }
+
+    function stopRecording() {
+      mediaRecorder.stop();
+      stream.getTracks().forEach(track => track.stop());
+      document.getElementById("status").innerText = "✅ 錄音完成，請稍候...";
+    }
+
+    function setup() {
+      const startBtn = document.getElementById("startBtn");
+      const stopBtn = document.getElementById("stopBtn");
+      startBtn.onclick = startRecording;
+      stopBtn.onclick = stopRecording;
+    }
+
+    window.addEventListener("DOMContentLoaded", setup);
+  </script>
+  <div>
+    <button id="startBtn">▶️ 開始錄音</button>
+    <button id="stopBtn">⏹️ 停止錄音</button>
+    <p id="status">等待開始錄音...</p>
+  </div>
+""", height=180)
+
+# Handle base64 audio input
+base64_audio = st.experimental_get_query_params().get("audio", [None])[0]
+audio_data = st.experimental_get_query_params().get("audio_data", [None])[0]
+
+if '_RECORDING_AUDIO_' not in st.session_state:
+    st.session_state._RECORDING_AUDIO_ = None
+
+def _handle_audio_recorder():
+    from streamlit.runtime.scriptrunner import get_script_run_ctx
+    import streamlit.runtime.legacy_caching as legacy_caching
+
+    ctx = get_script_run_ctx()
+    if ctx is None:
+        return
+
+    import streamlit.runtime.scriptrunner.script_run_context as src
+    from streamlit.runtime.uploaded_file_manager import UploadedFile
+
+    # Attach JS callback
+    components.html("""
+    <script>
+      window.addEventListener("streamlit:recordedAudio", function(e) {
+        const audioData = e.detail;
+        const form = document.createElement("form");
+        form.method = "POST";
+        form.action = window.location.href.split("?")[0];
+        form.innerHTML = `<input type="hidden" name="audio_data" value="${audioData}">`;
+        document.body.appendChild(form);
+        form.submit();
+      });
+    </script>
+    """, height=0)
+
+    if audio_data:
+        # Decode and save to temp file
+        audio_bytes = base64.b64decode(audio_data)
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".webm") as f:
+            f.write(audio_bytes)
+            f.flush()
+            st.session_state._RECORDING_AUDIO_ = f.name
+            st.success("✅ 錄音完成！")
+
+_handle_audio_recorder()
+
+# Use audio file if recorded
+text_input = ""
+if st.session_state._RECORDING_AUDIO_:
+    st.audio(st.session_state._RECORDING_AUDIO_)
+    with st.spinner("🧠 Whisper 處理語音..."):
+        transcription = whisper_model(st.session_state._RECORDING_AUDIO_)["text"]
+        text_input = transcription
+        st.success(f"📝 語音轉文字：{transcription}")
+
+# Manual fallback
+text_input = st.text_input("或手動輸入語句", value=text_input)
+
+if text_input and st.button("🚀 送出"):
+    with st.spinner("預測中..."):
+        result = predict_intent(text_input, model_id)
+        st.success(result)

requirements.txt

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+sentencepiece
+transformers>=4.30.0
+torch
+gradio
+safetensors
+huggingface_hub
+torchaudio
+gradio>=3.30.0