Update app.py

app.py

@@ -1,57 +1,27 @@
 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)
+# 設置 Hugging Face Cache 路徑
 os.environ["HF_HOME"] = "./cache"
 
-app.config["UPLOAD_FOLDER"] = UPLOAD_FOLDER
-
-# 載入 Hugging Face 模型
+# 加載 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)
+    return {"message": "Welcome to the Emotional Assistant!"}
 
-    # 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)
+@app.route("/analyze_emotion", methods=["POST"])
+def analyze_emotion():
+    user_input = request.json.get("message", "")
+    if not user_input:
+        return jsonify({"error": "No input provided"}), 400
 
-    return jsonify({
-        "transcription": transcribed_text,
-        "emotions": emotions,
-        "advice_text": advice_text,
-        "advice_audio": f"/{advice_audio_path}"
-    })
+    emotions = emotion_analysis(user_input)
+    return jsonify({"result": emotions})
 
 if __name__ == "__main__":
-    app.run(debug=True)
+    app.run(host="0.0.0.0", port=7860)