Upload app.py

app.py

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+from flask import Flask, render_template, request, jsonify
+from transformers import pipeline
+import os
+from werkzeug.utils import secure_filename
+
+app = Flask(__name__)
+
+# 設定上傳目錄與 Hugging Face 快取
+UPLOAD_FOLDER = "static/uploads"
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+os.environ["HF_HOME"] = "./cache"
+
+app.config["UPLOAD_FOLDER"] = UPLOAD_FOLDER
+
+# 載入 Hugging Face 模型
+emotion_analysis = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")
+text_to_speech = pipeline("text-to-speech", model="espnet/kan-bayashi_ljspeech_vits")
+
+# 首頁路由
+@app.route("/")
+def home():
+    return render_template("index.html")
+
+# 接收語音檔案並分析
+@app.route("/upload_audio", methods=["POST"])
+def upload_audio():
+    if "file" not in request.files:
+        return jsonify({"error": "No file part"}), 400
+
+    file = request.files["file"]
+    if file.filename == "":
+        return jsonify({"error": "No selected file"}), 400
+
+    # 儲存檔案
+    filename = secure_filename(file.filename)
+    filepath = os.path.join(app.config["UPLOAD_FOLDER"], filename)
+    file.save(filepath)
+
+    # 使用語音轉文字工具（假設使用預處理工具生成文本）
+    transcribed_text = "This is a placeholder for transcribed audio text."  # 替換為實際語音轉文字工具
+    emotions = emotion_analysis(transcribed_text)
+
+    # 生成語音建議
+    advice_text = "Based on your tone and words, you may want to relax and open up to others."
+    speech_output = text_to_speech(advice_text)
+    advice_audio_path = os.path.join(app.config["UPLOAD_FOLDER"], "advice_output.wav")
+    speech_output.save(advice_audio_path)
+
+    return jsonify({
+        "transcription": transcribed_text,
+        "emotions": emotions,
+        "advice_text": advice_text,
+        "advice_audio": f"/{advice_audio_path}"
+    })
+
+if __name__ == "__main__":
+    app.run(debug=True)