Update app.py

app.py

@@ -192,6 +192,58 @@ def ask():
 
     return jsonify(format_answers(question, answer, results))
 
+from flask import send_file
+import tempfile
+
+@app.route("/voice", methods=["POST"])
+def voice_chat():
+    try:
+        audio = request.files.get("audio")
+        if not audio:
+            return jsonify({"error": "No audio file uploaded"}), 400
+
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
+            audio.save(tmp.name)
+            audio_path = tmp.name
+
+        # Step 1️⃣: Transcribe using OpenAI Whisper or any STT
+        transcription = client.audio.transcriptions.create(
+            model="whisper-1",
+            file=open(audio_path, "rb")
+        )
+        text = transcription.text.strip()
+        print(f"🎤 Transcribed: {text}")
+
+        # Step 2️⃣: Get mentoring answer from your existing /ask logic
+        data = {"question": text}
+        with app.test_request_context(json=data):
+            response = ask()
+        response_json = response.get_json()
+
+        # Step 3️⃣: Optional TTS response
+        answer_text = response_json["answers"][0]["answer"]
+        speech_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
+        with client.audio.speech.with_streaming_response.create(
+            model="gpt-4o-mini-tts",
+            voice="alloy",
+            input=answer_text
+        ) as speech:
+            speech.stream_to_file(speech_file.name)
+
+        return jsonify({
+            "transcript": text,
+            "answer": answer_text,
+            "audio_url": f"/audio/{os.path.basename(speech_file.name)}"
+        })
+    except Exception as e:
+        traceback.print_exc()
+        return jsonify({"error": str(e)}), 500
+
+
+@app.route("/audio/<filename>")
+def serve_audio(filename):
+    return send_file(os.path.join(tempfile.gettempdir(), filename), mimetype="audio/mpeg")
+
 # ---------- Run ----------
 if __name__ == "__main__":
     port = int(os.environ.get("PORT", 7860))