initial commit

backend/app/services/instruction_service.py

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+"""FluentGerman.ai — Instruction assembly service."""
+
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.models import Instruction, InstructionType
+
+
+async def get_system_prompt(db: AsyncSession, user_id: int) -> str:
+    """Assemble the full system prompt from global + personal + homework instructions."""
+
+    # Global instructions (no user_id)
+    result = await db.execute(
+        select(Instruction).where(
+            Instruction.user_id.is_(None),
+            Instruction.type == InstructionType.GLOBAL,
+        )
+    )
+    global_instructions = result.scalars().all()
+
+    # Personal + homework for this user
+    result = await db.execute(
+        select(Instruction).where(Instruction.user_id == user_id)
+    )
+    user_instructions = result.scalars().all()
+
+    parts: list[str] = []
+
+    if global_instructions:
+        parts.append("=== TEACHING METHOD ===")
+        for inst in global_instructions:
+            parts.append(f"[{inst.title}]\n{inst.content}")
+
+    personal = [i for i in user_instructions if i.type == InstructionType.PERSONAL]
+    if personal:
+        parts.append("\n=== PERSONAL INSTRUCTIONS ===")
+        for inst in personal:
+            parts.append(f"[{inst.title}]\n{inst.content}")
+
+    homework = [i for i in user_instructions if i.type == InstructionType.HOMEWORK]
+    if homework:
+        parts.append("\n=== CURRENT HOMEWORK ===")
+        for inst in homework:
+            parts.append(f"[{inst.title}]\n{inst.content}")
+
+    if not parts:
+        return (
+            "You are a helpful German language tutor. Help the student learn German "
+            "through conversation, corrections, and explanations."
+        )
+
+    return "\n\n".join(parts)

backend/app/services/llm_service.py

@@ -0,0 +1,65 @@
+"""FluentGerman.ai — LLM service (provider-agnostic via LiteLLM)."""
+
+import logging
+from collections.abc import AsyncGenerator
+
+import litellm
+
+from app.config import get_settings
+
+logger = logging.getLogger("fluentgerman.llm")
+
+
+def _resolve_model(model: str, provider: str) -> str:
+    """Ensure Gemini models have the 'gemini/' prefix for Google AI Studio."""
+    if provider == "gemini" and not model.startswith("gemini/"):
+        resolved = f"gemini/{model}"
+        logger.info("Auto-prefixed model: %s → %s (Google AI Studio)", model, resolved)
+        return resolved
+    return model
+
+
+async def chat_stream(
+    messages: list[dict[str, str]],
+    model: str | None = None,
+) -> AsyncGenerator[str, None]:
+    """Stream chat completion tokens from the configured LLM provider."""
+    settings = get_settings()
+    model = _resolve_model(model or settings.llm_model, settings.llm_provider)
+
+    logger.info("LLM request: model=%s messages=%d", model, len(messages))
+
+    response = await litellm.acompletion(
+        model=model,
+        messages=messages,
+        api_key=settings.llm_api_key,
+        stream=True,
+    )
+
+    async for chunk in response:
+        delta = chunk.choices[0].delta
+        if delta.content:
+            yield delta.content
+
+
+async def summarize_instruction(raw_text: str) -> str:
+    """Ask the LLM to distill raw voice transcript into a structured instruction."""
+    settings = get_settings()
+    model = _resolve_model(settings.llm_model, settings.llm_provider)
+
+    meta_prompt = (
+        "You are an expert assistant for a language teacher. "
+        "The following text is a raw transcript of the teacher describing a learning instruction, "
+        "homework, or teaching method. Distill it into a clear, concise, structured instruction "
+        "that can be used as a system prompt for a language-learning LLM assistant. "
+        "Output ONLY the instruction text, no preamble.\n\n"
+        f"Transcript:\n{raw_text}"
+    )
+
+    response = await litellm.acompletion(
+        model=model,
+        messages=[{"role": "user", "content": meta_prompt}],
+        api_key=settings.llm_api_key,
+    )
+
+    return response.choices[0].message.content.strip()

backend/app/services/voice_service.py

@@ -0,0 +1,37 @@
+"""FluentGerman.ai — Voice service (API provider + browser fallback)."""
+
+import io
+
+import openai
+
+from app.config import get_settings
+
+
+async def transcribe(audio_bytes: bytes, filename: str = "audio.webm") -> str:
+    """Transcribe audio to text using OpenAI Whisper API."""
+    settings = get_settings()
+    client = openai.AsyncOpenAI(api_key=settings.llm_api_key)
+
+    audio_file = io.BytesIO(audio_bytes)
+    audio_file.name = filename
+
+    transcript = await client.audio.transcriptions.create(
+        model=settings.stt_model,
+        file=audio_file,
+    )
+    return transcript.text
+
+
+async def synthesize(text: str) -> bytes:
+    """Synthesize text to speech using OpenAI TTS API."""
+    settings = get_settings()
+    client = openai.AsyncOpenAI(api_key=settings.llm_api_key)
+
+    response = await client.audio.speech.create(
+        model=settings.tts_model,
+        voice=settings.tts_voice,
+        input=text,
+        response_format="mp3",
+    )
+
+    return response.content