fix

.env

@@ -11,8 +11,8 @@ DID_API_KEY=cmFqYWxhc2htaS5uQHB5a2FyYS5uZXQ:J2uPGx3uD4L7UKgHEiMJI
 DID_SOURCE_IMAGE_URL=https://i.ibb.co/Tpq77ZJ/teacher.png
 DID_VOICE_ID=en-US-JennyNeural
 TESSERACT_CMD=C:\Program Files\Tesseract-OCR\tesseract.exe
-CHROMA_DIR=C:/Users/DELL/Desktop/Deploymnet/29 oct/py-learn-backend/ragg/chroma
-CHROMA_ROOT=C:/Users/DELL/Desktop/Deploymnet/29 oct/py-learn-backend/ragg/chroma
+CHROMA_DIR=C:/Users/DELL/Desktop/Deploymnet/29 oct/mj-learn-backend/ragg/chroma
+CHROMA_ROOT=C:/Users/DELL/Desktop/Deploymnet/29 oct/mj-learn-backend/ragg/chroma
 EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2
 ALLOWED_ORIGINS=http://localhost:4200,http://127.0.0.1:4200
 RAG_INGEST_URL=http://localhost:5000/rag/ingest

ragg/app.py

@@ -332,6 +332,115 @@ def rag_generate_questions():
 
 
 # @rag_bp.route("/explain-grammar", methods=["POST", "OPTIONS"])
+# @rag_bp.route("/explain-grammar", methods=["POST", "OPTIONS"])
+# def rag_explain_grammar():
+#     if request.method == "OPTIONS":
+#         return ("", 204)
+
+#     data = request.get_json(force=True) or {}
+
+#     # --- Extract username and db_level ---
+#     username = extract_username_from_request(request)
+#     db_level = user_to_db_level(username)
+
+#     # --- MAIN BODY (your preferred structure) ---
+#     body = ExplainBody(
+#         question=(data.get("question") or "").strip(),
+#         model=data.get("model", "gpt-4o-mini"),
+#         db_level=db_level,
+#         source_ids=data.get("source_ids") or []
+#     )
+
+#     # --- 1) Run LLM / RAG explanation ---
+#     result_raw = llm_explain(body)
+
+#     # --- 2) Normalize + extract answer safely ---
+#     result_dict = None
+#     answer_text = ""
+#     try:
+#         if isinstance(result_raw, dict):
+#             result_dict = dict(result_raw)
+#         elif hasattr(result_raw, "model_dump"):
+#             result_dict = result_raw.model_dump()
+#         elif hasattr(result_raw, "dict"):
+#             result_dict = result_raw.dict()
+#         elif isinstance(result_raw, str):
+#             result_dict = {"answer": result_raw}
+#         else:
+#             result_dict = {"answer": str(result_raw)}
+
+#         answer_text = (
+#             result_dict.get("answer")
+#             or result_dict.get("response")
+#             or result_dict.get("text")
+#             or ""
+#         ).strip()
+#     except Exception as e:
+#         current_app.logger.exception("Failed to normalize llm_explain result: %s", e)
+#         return jsonify({"error": "Internal error normalizing LLM response"}), 500
+
+#     # --- 3) Optional: synthesize TTS audio ---
+#     try:
+#         if data.get("synthesize_audio"):
+#             try:
+#                 out_name = f"explain_{uuid.uuid4().hex}.wav"
+#                 wav_path = xtts_speak_to_file(
+#                     text=answer_text or result_dict.get("answer", ""),
+#                     out_file=AUDIO_DIR / out_name,
+#                     reference_dir=XTTS_REF_DIR,
+#                     reference_files=None,
+#                     language=data.get("language", "en"),
+#                 )
+#                 # Local: serve from /rag/audio/*
+#                 if "localhost" in request.host_url or "127.0.0.1" in request.host_url:
+#                     base = request.host_url.rstrip("/")
+#                     result_dict["audio_url"] = f"{base}/rag/audio/{wav_path.name}"
+#                 else:
+#                     # Deployed: try S3 first; fallback to public SPACE_URL if set
+#                     s3_url = _upload_to_s3(str(wav_path))
+#                     if s3_url:
+#                         result_dict["audio_url"] = s3_url
+#                     else:
+#                         base = os.getenv("SPACE_URL", "https://pykara-py-learn-backend.hf.space")
+#                         result_dict["audio_url"] = f"{base}/rag/audio/{wav_path.name}"
+#             except FileNotFoundError as e:
+#                 current_app.logger.error("XTTS reference audio missing: %s", e)
+#             except Exception as e:
+#                 current_app.logger.exception("XTTS synthesis during explain-grammar failed: %s", e)
+#     except Exception:
+#         current_app.logger.exception("Unexpected error while attempting inline synthesis")
+
+#     # --- 4) Optional: synthesize video (D-ID) ---
+#     try:
+#         if data.get("synthesize_video"):
+#             if not DID_API_KEY or not DID_SOURCE_IMAGE_URL:
+#                 current_app.logger.error("D-ID not configured for inline explain-grammar video synthesis")
+#             else:
+#                 try:
+#                     talk_id, err = _did_create_talk(answer_text or result_dict.get("answer", ""))
+#                     if err:
+#                         current_app.logger.error(
+#                             "D-ID create error during explain-grammar: %s",
+#                             err[0] if isinstance(err, tuple) else err,
+#                         )
+#                     else:
+#                         video_url, err = _did_poll_talk(talk_id, timeout_sec=120, interval_sec=2.0)
+#                         if err:
+#                             current_app.logger.error(
+#                                 "D-ID poll error during explain-grammar: %s",
+#                                 err[0] if isinstance(err, tuple) else err,
+#                             )
+#                         else:
+#                             if video_url:
+#                                 result_dict["video_url"] = video_url
+#                 except Exception as e:
+#                     current_app.logger.exception("D-ID inline synthesis failed during explain-grammar: %s", e)
+#     except Exception:
+#         current_app.logger.exception("Unexpected error while attempting inline video synthesis")
+
+#     # --- Final response ---
+#     return jsonify(result_dict), 200
+
 @rag_bp.route("/explain-grammar", methods=["POST", "OPTIONS"])
 def rag_explain_grammar():
     if request.method == "OPTIONS":
@@ -391,18 +500,8 @@ def rag_explain_grammar():
                     reference_files=None,
                     language=data.get("language", "en"),
                 )
-                # Local: serve from /rag/audio/*
-                if "localhost" in request.host_url or "127.0.0.1" in request.host_url:
-                    base = request.host_url.rstrip("/")
-                    result_dict["audio_url"] = f"{base}/rag/audio/{wav_path.name}"
-                else:
-                    # Deployed: try S3 first; fallback to public SPACE_URL if set
-                    s3_url = _upload_to_s3(str(wav_path))
-                    if s3_url:
-                        result_dict["audio_url"] = s3_url
-                    else:
-                        base = os.getenv("SPACE_URL", "https://pykara-py-learn-backend.hf.space")
-                        result_dict["audio_url"] = f"{base}/rag/audio/{wav_path.name}"
+                base = request.host_url.rstrip("/")
+                result_dict["audio_url"] = f"{base}/rag/audio/{wav_path.name}"
             except FileNotFoundError as e:
                 current_app.logger.error("XTTS reference audio missing: %s", e)
             except Exception as e: