initial commic

app.py

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+from flask import Flask, request, jsonify, send_file
+import os, uuid, time
+from pathlib import Path
+import requests
+
+app = Flask(__name__)
+
+# --- Config via environment variables in Space settings ---
+HF_TOKEN = os.getenv("HF_TOKEN", "")        # required
+HF_STT_MODEL = os.getenv("HF_STT_MODEL", "openai/whisper-small")   # speech-to-text
+HF_LLM_MODEL = os.getenv("HF_LLM_MODEL", "google/flan-t5-large")   # text generation
+HF_TTS_MODEL = os.getenv("HF_TTS_MODEL", "espnet/kan-bayashi_ljspeech_vits")  # text-to-speech (example)
+
+TMP = Path("tmp")
+TMP.mkdir(exist_ok=True)
+
+if not HF_TOKEN:
+    print("WARNING: HF_TOKEN not set. Set it in Space Settings -> Secrets/Vars.")
+
+HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"}
+
+def hf_inference_raw(model_id: str, input_data, headers_extra=None, as_json=True, timeout=120):
+    url = f"https://api-inference.huggingface.co/models/{model_id}"
+    headers = HEADERS.copy()
+    if headers_extra:
+        headers.update(headers_extra)
+    if isinstance(input_data, (bytes, bytearray)):
+        resp = requests.post(url, headers=headers, data=input_data, timeout=timeout)
+    else:
+        resp = requests.post(url, headers=headers, json=input_data, timeout=timeout)
+    resp.raise_for_status()
+    if as_json:
+        try:
+            return resp.json()
+        except Exception:
+            return resp.text
+    else:
+        return resp.content
+
+def stt_from_wav_bytes(wav_bytes: bytes):
+    # Many whisper-based HF endpoints accept audio/wav or audio/mpeg
+    try:
+        out = hf_inference_raw(HF_STT_MODEL, wav_bytes, headers_extra={"Content-Type":"audio/wav"}, as_json=True, timeout=300)
+        if isinstance(out, dict) and "text" in out:
+            return out["text"]
+        if isinstance(out, str):
+            return out
+        # fallback
+        return str(out)
+    except Exception as e:
+        print("STT error:", e)
+        return ""
+
+def llm_query(prompt: str):
+    payload = {"inputs": prompt}
+    try:
+        out = hf_inference_raw(HF_LLM_MODEL, payload, as_json=True, timeout=120)
+        # handle common shapes
+        if isinstance(out, list) and len(out) > 0:
+            first = out[0]
+            if isinstance(first, dict) and "generated_text" in first:
+                return first["generated_text"]
+            return str(first)
+        if isinstance(out, dict):
+            if "generated_text" in out:
+                return out["generated_text"]
+            if "text" in out:
+                return out["text"]
+            return str(out)
+        if isinstance(out, str):
+            return out
+        return str(out)
+    except Exception as e:
+        print("LLM error:", e)
+        return "Xin lỗi, tôi gặp lỗi khi trả lời."
+
+def tts_to_wav_bytes(text: str):
+    payload = {"inputs": text}
+    try:
+        # request raw bytes
+        url = f"https://api-inference.huggingface.co/models/{HF_TTS_MODEL}"
+        resp = requests.post(url, headers=HEADERS, json=payload, timeout=120)
+        resp.raise_for_status()
+        return resp.content
+    except Exception as e:
+        print("TTS error:", e, getattr(e, 'response', None) and e.response.text)
+        return b""
+
+@app.route("/health")
+def health():
+    return jsonify({"ok": True})
+
+@app.route("/upload_audio", methods=["POST"])
+def upload_audio():
+    try:
+        audio_bytes = request.get_data()
+        if not audio_bytes:
+            return jsonify({"error":"no audio data"}), 400
+        fname = f"{int(time.time())}_{uuid.uuid4().hex}.wav"
+        path = TMP / fname
+        path.write_bytes(audio_bytes)
+
+        # 1) STT
+        text = stt_from_wav_bytes(audio_bytes)
+        if not text:
+            text = "(Không nhận được lời nói)"
+
+        # 2) LLM
+        prompt = f"Bạn là trợ lý. Người dùng nói: '{text}'. Trả lời ngắn gọn bằng tiếng Việt."
+        reply = llm_query(prompt)
+
+        # 3) TTS
+        tts_bytes = tts_to_wav_bytes(reply)
+        tts_fname = ""
+        if tts_bytes:
+            tts_fname = f"tts_{int(time.time())}_{uuid.uuid4().hex}.wav"
+            (TMP / tts_fname).write_bytes(tts_bytes)
+
+        return jsonify({"text": text, "reply": reply, "tts_file": tts_fname})
+    except Exception as e:
+        print("upload_audio error:", e)
+        return jsonify({"error": str(e)}), 500
+
+@app.route("/ask", methods=["POST"])
+def ask():
+    try:
+        data = request.get_json(force=True)
+        text = data.get("text", "")
+        if not text:
+            return jsonify({"error":"no text"}), 400
+        reply = llm_query(text)
+        tts_bytes = tts_to_wav_bytes(reply)
+        tts_fname = ""
+        if tts_bytes:
+            tts_fname = f"tts_{int(time.time())}_{uuid.uuid4().hex}.wav"
+            (TMP / tts_fname).write_bytes(tts_bytes)
+        return jsonify({"text": reply, "tts_file": tts_fname})
+    except Exception as e:
+        print("ask error:", e)
+        return jsonify({"error": str(e)}), 500
+
+@app.route("/tts/<fname>", methods=["GET"])
+def serve_tts(fname):
+    p = TMP / fname
+    if not p.exists():
+        return "Not found", 404
+    return send_file(str(p), mimetype="audio/wav", as_attachment=False)
+
+if __name__ == "__main__":
+    port = int(os.getenv("PORT", "7860"))
+    app.run(host="0.0.0.0", port=port, threaded=True)

requirements.txt

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+flask
+requests