Update app.py

app.py

@@ -1,79 +1,62 @@
-from flask import Flask, request, jsonify, send_file
-from flask_cors import CORS
-from faster_whisper import WhisperModel
+from flask import Flask, request, jsonify, send\_file
+from flask\_cors import CORS
+from faster\_whisper import WhisperModel
 from transformers import pipeline
 from TTS.api import TTS
 import tempfile
 import os
 
-app = Flask(__name__)
+app = Flask(**name**)
 CORS(app)
 
-# Load models once at startup
-whisper_model = WhisperModel("small", device="cpu", compute_type="int8")
-llm = pipeline("text-generation", model="tiiuae/falcon-rw-1b", max_new_tokens=100)
-tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC", progress_bar=False, gpu=False)
+# Load models
 
-@app.route("/talk", methods=["POST"])
+whisper\_model = WhisperModel("small", device="cpu", compute\_type="int8")
+llm = pipeline("text-generation", model="tiiuae/falcon-rw-1b", max\_new\_tokens=100)
+tts = TTS(model\_name="tts\_models/en/ljspeech/tacotron2-DDC", progress\_bar=False, gpu=False)
+
+@app.route("/talk", methods=\["POST"])
 def talk():
-    if "audio" not in request.files:
-        return jsonify({"error": "No audio file"}), 400
-    
-    # Save audio
-    audio_file = request.files["audio"]
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
-        audio_path = tmp.name
-        audio_file.save(audio_path)
-    
-    # Transcribe
-    segments, _ = whisper_model.transcribe(audio_path)
-    transcription = "".join([seg.text for seg in segments])
-    
-    # Generate response with a clear prompt format
-    prompt = f"User: {transcription}\nAI:"
-    response_raw = llm(prompt)[0]["generated_text"]
-    
-    # Extract only the AI's response (everything after "AI:")
-    try:
-        ai_response = response_raw.split("AI:")[1].strip()
-    except:
-        # Fallback if splitting fails
-        ai_response = response_raw
-    
-    # Synthesize speech using only the AI's response
-    tts_audio_path = audio_path.replace(".wav", "_reply.wav")
-    tts.tts_to_file(text=ai_response, file_path=tts_audio_path)
-    
-    # Clean up the original audio file
-    try:
-        os.unlink(audio_path)
-    except:
-        pass
-        
-    return send_file(tts_audio_path, mimetype="audio/wav")
+if "audio" not in request.files:
+return jsonify({"error": "No audio file"}), 400
+
+```
+# Save audio
+audio_file = request.files["audio"]
+with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
+    audio_path = tmp.name
+    audio_file.save(audio_path)
+
+# Transcribe
+segments, _ = whisper_model.transcribe(audio_path)
+transcription = "".join([seg.text for seg in segments])
+
+# Generate response
+response_text = llm(transcription)[0]["generated_text"]
 
-@app.route("/chat", methods=["POST"])
+# Synthesize speech
+tts_audio_path = audio_path.replace(".wav", "_reply.wav")
+tts.tts_to_file(text=response_text, file_path=tts_audio_path)
+
+return send_file(tts_audio_path, mimetype="audio/wav")
+```
+
+@app.route("/chat", methods=\["POST"])
 def chat():
-    data = request.get_json()
-    if not data or "text" not in data:
-        return jsonify({"error": "Missing 'text' in request body"}), 400
-    
-    user_input = data["text"]
-    
-    # Same improvement for text chat
-    prompt = f"User: {user_input}\nAI:"
-    response_raw = llm(prompt)[0]["generated_text"]
-    
-    try:
-        ai_response = response_raw.split("AI:")[1].strip()
-    except:
-        ai_response = response_raw
-    
-    return jsonify({"response": ai_response})
+data = request.get\_json()
+if not data or "text" not in data:
+return jsonify({"error": "Missing 'text' in request body"}), 400
+
+```
+user_input = data["text"]
+response = llm(user_input)[0]["generated_text"]
+
+return jsonify({"response": response})
+```
 
 @app.route("/")
 def index():
-    return "Metaverse AI Character API running."
+return "Metaverse AI Character API running."
 
-if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=7860)
+if **name** == "**main**":
+app.run(host="0.0.0.0", port=7860)