api.py

import os
import pandas as pd
from datetime import datetime
import tempfile
import threading
import uuid
from fastapi import FastAPI, HTTPException, UploadFile, File, Form
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field
from openai import AsyncOpenAI
from dotenv import load_dotenv
from src.dialect_rules import (
    hausa_variety_instruction,
    nigerian_variety_instruction,
    nigerian_variety_retry_prompt,
    nigerian_variety_retry_reason,
)

load_dotenv()

app = FastAPI(title="PurePolyglot Hybrid Backend", version="1.0.0")

# Enable CORS for the Vite SPA
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Attempt Qwen first, fallback to Groq
QWEN_API_KEY = os.getenv("QWEN_API_KEY")
QWEN_BASE_URL = os.getenv("QWEN_BASE_URL", "https://dashscope-intl.aliyuncs.com/compatible-mode/v1")
QWEN_MODEL_NAME = os.getenv("QWEN_MODEL_NAME", "qwen3-coder-80b-instruct")

GROQ_API_KEY = os.getenv("GROQ_API_KEY")
GROQ_MODEL_NAME = os.getenv("GROQ_MODEL_NAME", "llama-3.3-70b-versatile")
DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY")
DEEPSEEK_BASE_URL = os.getenv("DEEPSEEK_BASE_URL", "https://api.deepseek.com")
DEEPSEEK_MODEL_NAME = os.getenv("DEEPSEEK_MODEL_NAME", "deepseek-chat")
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
GEMINI_BASE_URL = os.getenv("GEMINI_BASE_URL", "https://generativelanguage.googleapis.com/v1beta/openai/")
GEMINI_MODEL_NAME = os.getenv("GEMINI_MODEL_NAME", "gemini-2.5-flash")
OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY")
OPENROUTER_BASE_URL = os.getenv("OPENROUTER_BASE_URL", "https://openrouter.ai/api/v1")
OPENROUTER_FREE_MODEL_NAME = os.getenv("OPENROUTER_FREE_MODEL_NAME", "openrouter/free")
OPENROUTER_NEMOTRON_MODEL_NAME = os.getenv("OPENROUTER_NEMOTRON_MODEL_NAME", "nvidia/nemotron-3-nano-30b-a3b:free")
OPENROUTER_GPT_OSS_MODEL_NAME = os.getenv("OPENROUTER_GPT_OSS_MODEL_NAME", "openai/gpt-oss-20b:free")
OPENROUTER_LFM_MODEL_NAME = os.getenv("OPENROUTER_LFM_MODEL_NAME", "liquid/lfm-2.5-1.2b-instruct:free")

AI_ROUTES = {}
if QWEN_API_KEY and QWEN_API_KEY != "your-api-key-here":
    AI_ROUTES["qwen"] = (AsyncOpenAI(api_key=QWEN_API_KEY, base_url=QWEN_BASE_URL), QWEN_MODEL_NAME, "Qwen Hybrid Node")
if GROQ_API_KEY:
    AI_ROUTES["llama"] = (AsyncOpenAI(api_key=GROQ_API_KEY, base_url="https://api.groq.com/openai/v1"), GROQ_MODEL_NAME, "Groq Llama Hybrid Node")
if DEEPSEEK_API_KEY:
    AI_ROUTES["deepseek"] = (AsyncOpenAI(api_key=DEEPSEEK_API_KEY, base_url=DEEPSEEK_BASE_URL), DEEPSEEK_MODEL_NAME, "DeepSeek Hybrid Node")
if GEMINI_API_KEY:
    AI_ROUTES["gemini"] = (AsyncOpenAI(api_key=GEMINI_API_KEY, base_url=GEMINI_BASE_URL), GEMINI_MODEL_NAME, "Gemini Hybrid Node")
if OPENROUTER_API_KEY:
    openrouter_client = AsyncOpenAI(api_key=OPENROUTER_API_KEY, base_url=OPENROUTER_BASE_URL)
    AI_ROUTES["openrouter-free"] = (openrouter_client, OPENROUTER_FREE_MODEL_NAME, "OpenRouter Free Node")
    AI_ROUTES["nemotron"] = (openrouter_client, OPENROUTER_NEMOTRON_MODEL_NAME, "OpenRouter Nemotron Free Node")
    AI_ROUTES["gpt-oss"] = (openrouter_client, OPENROUTER_GPT_OSS_MODEL_NAME, "OpenRouter GPT-OSS Free Node")
    AI_ROUTES["lfm"] = (openrouter_client, OPENROUTER_LFM_MODEL_NAME, "OpenRouter LFM Free Node")

if "qwen" in AI_ROUTES:
    client, MODEL_NAME, NODE_TYPE = AI_ROUTES["qwen"]
elif "llama" in AI_ROUTES:
    client, MODEL_NAME, NODE_TYPE = AI_ROUTES["llama"]
elif "deepseek" in AI_ROUTES:
    client, MODEL_NAME, NODE_TYPE = AI_ROUTES["deepseek"]
elif "gemini" in AI_ROUTES:
    client, MODEL_NAME, NODE_TYPE = AI_ROUTES["gemini"]
elif "openrouter-free" in AI_ROUTES:
    client, MODEL_NAME, NODE_TYPE = AI_ROUTES["openrouter-free"]
else:
    client = None
    MODEL_NAME = None
    NODE_TYPE = "Offline"

def resolve_ai_route(ai_model: str, source_label: str, target_label: str, text: str):
    choice = (ai_model or "auto").strip().lower()
    if choice == "auto":
        hint = f"{source_label} {target_label} {text}".lower()
        if any(token in hint for token in [
            "korean", "hangul", "chinese", "mandarin", "cantonese", "arabic",
            "japanese", "thai", "vietnamese", "code-switch", "multilingual"
        ]):
            choice = "qwen"
        elif any(token in hint for token in [
            "reason", "explain", "ambiguity", "semantic", "pragmatic", "cultural", "review", "oracle"
        ]):
            choice = "nemotron"
        else:
            choice = "llama"

    ordered = [choice, "qwen", "llama", "nemotron", "gpt-oss", "lfm", "openrouter-free", "deepseek", "gemini"]
    for route_name in ordered:
        if route_name in AI_ROUTES:
            return AI_ROUTES[route_name]
    return client, MODEL_NAME, NODE_TYPE

class TranslationRequest(BaseModel):
    text: str
    source_language: str = "Unknown"
    source_dialect: str = "Standard"
    target_language: str
    target_dialect: str
    user_key: str = "Polyglot Player"
    ai_model: str = "auto"

class TranslationResponse(BaseModel):
    original_text: str
    translated_text: str
    target_dialect: str
    node: str

class PolyglotReviewSubmission(BaseModel):
    interaction_id: str = Field(min_length=8, max_length=128)
    supersedes_interaction_id: str = Field(default="", max_length=128)
    app_source: str = Field(default="PurePolyglot", min_length=2, max_length=64)
    user_key: str = Field(default="Polyglot Player", max_length=256)
    source_text: str = Field(min_length=1, max_length=10000)
    source_input_mode: str = Field(default="text", max_length=32)
    machine_transcript_initial: str = Field(default="", max_length=10000)
    user_transcript_final: str = Field(default="", max_length=10000)
    machine_translation_initial: str = Field(min_length=1, max_length=10000)
    user_translation_final: str = Field(min_length=1, max_length=10000)
    source_language: str = Field(default="Unknown", max_length=128)
    source_dialect: str = Field(default="Standard", max_length=256)
    target_language: str = Field(default="Unknown", max_length=128)
    target_dialect: str = Field(default="Standard", max_length=256)
    asr_model: str = Field(default="", max_length=128)
    audio_sanitation: bool = False
    ai_model: str = Field(default="auto", max_length=128)
    translation_route: str = Field(default="frontend-reviewed", max_length=128)
    consent_confirmed: bool = False
    consent_version: str = Field(default="polyglot-reviewed-submit-v1", max_length=128)


_PENDING_QUEUE_LOCK = threading.Lock()


def _pending_queue_path():
    configured = os.environ.get("PENDING_APPROVALS_FILE", "").strip()
    if configured:
        return configured
    return "/app/pending_approvals.csv" if os.path.exists("/app") else "pending_approvals.csv"


def _translation_edit_distance(initial_text: str, final_text: str):
    initial = str(initial_text or "").casefold().split()
    final = str(final_text or "").casefold().split()
    if not initial and not final:
        return 0.0

    previous = list(range(len(final) + 1))
    for row_index, initial_token in enumerate(initial, start=1):
        current = [row_index]
        for column_index, final_token in enumerate(final, start=1):
            substitution_cost = 0 if initial_token == final_token else 1
            current.append(
                min(
                    current[-1] + 1,
                    previous[column_index] + 1,
                    previous[column_index - 1] + substitution_cost,
                )
            )
        previous = current

    return round(previous[-1] / max(len(initial), len(final), 1), 4)


def _sync_pending_queue_to_hub(pending_file: str, queue_id: str):
    hf_token = os.environ.get("HF_TOKEN")
    if not hf_token:
        return False

    from huggingface_hub import HfApi

    api = HfApi(token=hf_token)
    api.upload_file(
        path_or_fileobj=pending_file,
        path_in_repo="pending_approvals.csv",
        repo_id="toecm/PureChain_Dataset",
        repo_type="dataset",
        commit_message=f"Reviewed Polyglot Chat submission {queue_id}",
    )
    return True


def _append_polyglot_review(request: PolyglotReviewSubmission):
    pending_file = _pending_queue_path()
    submitted_at = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    queue_id = f"polyglot-{uuid.uuid4()}"
    final_source_text = (request.user_transcript_final or request.source_text).strip()
    new_entry = {
        # Legacy reviewer fields remain populated for existing admin tools.
        "User": request.user_key,
        "Data_Origin": "Game: Polyglot Chat",
        "Utterance": final_source_text,
        "Dialect": request.target_dialect.strip(),
        "Clarification": request.user_translation_final.strip(),
        "Clarification_Source": f"User-reviewed / {request.ai_model}",
        "Tone": "Neutral / Conversational",
        "Context": f"Translated from {request.source_language} ({request.source_dialect})",
        "Pragmatic_Analysis": "",
        "Audio": "",
        "Timestamp": submitted_at,
        "Chain_ID": "",
        "Approvers": "",
        "Language": request.target_language.strip(),
        # Reviewed-translation audit and fine-tuning fields.
        "Queue_ID": queue_id,
        "Interaction_ID": request.interaction_id.strip(),
        "Supersedes_Interaction_ID": request.supersedes_interaction_id.strip(),
        "App_Source": request.app_source.strip(),
        "Submission_Status": "Pending Review",
        "Consent_Confirmed": "true",
        "Consent_Version": request.consent_version.strip(),
        "Source_Language": request.source_language.strip(),
        "Source_Dialect": request.source_dialect.strip(),
        "Source_Input_Mode": request.source_input_mode.strip().lower() or "text",
        "Machine_Transcript_Initial": request.machine_transcript_initial.strip(),
        "User_Transcript_Final": final_source_text,
        "Transcript_Edit_Distance": _translation_edit_distance(
            request.machine_transcript_initial,
            final_source_text,
        ) if request.machine_transcript_initial.strip() else 0.0,
        "ASR_Model": request.asr_model.strip(),
        "Audio_Sanitation": str(request.audio_sanitation).lower(),
        "Audio_Retained": "false",
        "Target_Language": request.target_language.strip(),
        "Target_Dialect": request.target_dialect.strip(),
        "Machine_Translation_Initial": request.machine_translation_initial.strip(),
        "User_Translation_Final": request.user_translation_final.strip(),
        "Translation_Edit_Distance": _translation_edit_distance(
            request.machine_translation_initial,
            request.user_translation_final,
        ),
        "AI_Model": request.ai_model.strip(),
        "Translation_Route": request.translation_route.strip(),
        "Review_Submitted_At": submitted_at,
    }

    with _PENDING_QUEUE_LOCK:
        if os.path.exists(pending_file):
            df = pd.read_csv(pending_file, dtype=str).fillna("")
        else:
            parent = os.path.dirname(os.path.abspath(pending_file))
            os.makedirs(parent, exist_ok=True)
            df = pd.DataFrame()

        if "Interaction_ID" in df.columns:
            duplicate = df[df["Interaction_ID"].astype(str) == request.interaction_id.strip()]
            if not duplicate.empty:
                existing = duplicate.iloc[0]
                existing_final = str(
                    existing.get("User_Translation_Final", "")
                    or existing.get("Clarification", "")
                ).strip()
                if existing_final != request.user_translation_final.strip():
                    raise HTTPException(
                        status_code=409,
                        detail="This interaction ID already belongs to a different reviewed translation.",
                    )
                existing_queue_id = str(existing.get("Queue_ID", ""))
                synced_to_hub = _sync_pending_queue_to_hub(
                    pending_file,
                    existing_queue_id or request.interaction_id.strip(),
                )
                return {
                    "queued": True,
                    "duplicate": True,
                    "queue_id": existing_queue_id,
                    "status": str(existing.get("Submission_Status", "Pending Review")),
                    "synced_to_hub": synced_to_hub,
                }

        for column in new_entry:
            if column not in df.columns:
                df[column] = ""
        row = {column: new_entry.get(column, "") for column in df.columns}
        df.loc[len(df)] = row

        temp_file = f"{pending_file}.tmp"
        df.to_csv(temp_file, index=False)
        os.replace(temp_file, pending_file)
        synced_to_hub = _sync_pending_queue_to_hub(pending_file, queue_id)

    return {
        "queued": True,
        "duplicate": False,
        "queue_id": queue_id,
        "status": "Pending Review",
        "synced_to_hub": synced_to_hub,
    }


@app.post("/api/polyglot-chat/submit")
def submit_polyglot_review(request: PolyglotReviewSubmission):
    if not request.consent_confirmed:
        raise HTTPException(
            status_code=400,
            detail="Explicit consent is required before a translation can enter pending review.",
        )
    try:
        return _append_polyglot_review(request)
    except HTTPException:
        raise
    except Exception as exc:
        print(f"Failed to submit reviewed Polyglot Chat entry: {exc}")
        raise HTTPException(status_code=503, detail="Pending review submission failed.") from exc

@app.post("/api/translate", response_model=TranslationResponse)
async def translate_text(request: TranslationRequest):
    source_label = f"{request.source_language} ({request.source_dialect})"
    target_label = f"{request.target_language} ({request.target_dialect})"
    route_client, route_model, route_node = resolve_ai_route(request.ai_model, source_label, target_label, request.text)
    if not route_client:
        raise HTTPException(status_code=500, detail="No LLM API key configured for Qwen, Llama/Groq, OpenRouter, DeepSeek, or Gemini.")

    variety_instruction = "\n".join(filter(None, [
        nigerian_variety_instruction(source_label, target_label),
        hausa_variety_instruction(source_label, target_label),
    ]))

    system_prompt = (
        f"You are an expert polyglot interpreter specializing in deep cultural and linguistic dialects.\n"
        f"Translate the following text from {source_label} "
        f"into {target_label}.\n"
        f"Output ONLY the raw translated string. Do not include quotes, explanations, or thinking traces.\n"
        f"Use the target language's native writing system. Korean, Jeju, and Satoori outputs must use Hangul only, not romanization and not Chinese or Japanese characters. "
        f"Arabic outputs must use Arabic script. Igbo outputs must keep proper Igbo letters and tone/dot marks such as ị, ụ, ọ, ṅ, ẹ, á, and à where natural.\n"
        f"{variety_instruction}"
    )
    
    try:
        response = await route_client.chat.completions.create(
            model=route_model,
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": request.text}
            ],
            temperature=0.3,
            max_tokens=256
        )
        
        translated_text = response.choices[0].message.content.strip()
        boundary_reason = nigerian_variety_retry_reason(translated_text, target_label)
        if boundary_reason:
            retry_prompt = system_prompt + "\n" + nigerian_variety_retry_prompt(
                request.text, source_label, target_label, translated_text, boundary_reason
            )
            retry_response = await route_client.chat.completions.create(
                model=route_model,
                messages=[
                    {"role": "system", "content": retry_prompt},
                    {"role": "user", "content": request.text}
                ],
                temperature=0.2,
                max_tokens=256
            )
            retry_text = retry_response.choices[0].message.content.strip()
            if retry_text and not nigerian_variety_retry_reason(retry_text, target_label):
                translated_text = retry_text
        
        return TranslationResponse(
            original_text=request.text,
            translated_text=translated_text,
            target_dialect=f"{request.target_language} ({request.target_dialect})",
            node=route_node
        )
        
    except Exception as e:
        print(f"Error calling {route_node} API: {e}")
        raise HTTPException(status_code=500, detail=str(e))

def _get_shared_acoustic_agent():
    try:
        import sys

        main_module = sys.modules.get("main") or sys.modules.get("__main__")
        return getattr(main_module, "ACOUSTIC_AGENT", None)
    except Exception as e:
        print(f"Acoustic agent lookup failed: {e}")
        return None

@app.get("/api/acoustic/models")
async def acoustic_models():
    agent = _get_shared_acoustic_agent()
    if not agent or not hasattr(agent, "models"):
        return {
            "ok": False,
            "service": "pure-acoustic-agent",
            "error": "Acoustic agent unavailable. Run through backend/app.py or the Gradio app bootstrap.",
        }
    return agent.models()

@app.post("/api/acoustic/transcribe")
async def acoustic_transcribe(
    audio: UploadFile = File(...),
    language: str = Form(""),
    dialect: str = Form(""),
    speech_model: str = Form("auto"),
    audio_sanitation: str = Form("on"),
):
    agent = _get_shared_acoustic_agent()
    if not agent or not hasattr(agent, "transcribe"):
        raise HTTPException(status_code=503, detail="Acoustic agent unavailable.")

    suffix = os.path.splitext(audio.filename or "")[1] or ".webm"
    temp_path = None
    try:
        with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
            temp_path = tmp.name
            tmp.write(await audio.read())
        return agent.transcribe(
            temp_path,
            language=language,
            dialect=dialect,
            speech_model=speech_model,
            audio_sanitation=audio_sanitation,
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))
    finally:
        if temp_path and os.path.exists(temp_path):
            try:
                os.remove(temp_path)
            except Exception:
                pass

class AcousticTTSRequest(BaseModel):
    text: str = ""
    language: str = ""
    dialect: str = ""
    voice: str = "browser-native"

@app.post("/api/acoustic/tts")
async def acoustic_tts(request: AcousticTTSRequest):
    agent = _get_shared_acoustic_agent()
    if not agent or not hasattr(agent, "tts"):
        raise HTTPException(status_code=503, detail="Acoustic agent unavailable.")
    return agent.tts(
        request.text,
        language=request.language,
        dialect=request.dialect,
        voice=request.voice,
    )

@app.get("/api/health")
async def root():
    return {"message": f"PurePolyglot Hybrid Backend Online ({NODE_TYPE})"}

if __name__ == "__main__":
    import uvicorn
    uvicorn.run("api:app", host="0.0.0.0", port=8000, reload=True)