main.py

import os
import tempfile
import traceback
from fastapi import FastAPI, UploadFile, File, Header, HTTPException, Body
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from spitch import Spitch
from langchain.prompts import PromptTemplate
from langchain_huggingface import HuggingFaceEndpoint
from langdetect import detect, DetectorFactory
from huggingface_hub.utils import HfHubHTTPError
from smebuilder_vector import retriever  # Retriever for context injection

# ----------------- CONFIG -----------------
DetectorFactory.seed = 0

SPITCH_API_KEY = os.getenv("SPITCH_API_KEY")
HF_MODEL = os.getenv("HF_MODEL", "deepseek-ai/deepseek-coder-1.3b-instruct")
FRONTEND_ORIGIN = os.getenv("ALLOWED_ORIGIN", "*")
PROJECT_API_KEY = os.getenv("PROJECT_API_KEY")

if not SPITCH_API_KEY:
    raise RuntimeError("Set SPITCH_API_KEY in environment before starting.")

os.environ["SPITCH_API_KEY"] = SPITCH_API_KEY
spitch_client = Spitch()

# HuggingFace LLM
llm = HuggingFaceEndpoint(
    repo_id=HF_MODEL,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
    repetition_penalty=1.1,
    max_new_tokens=2048
)

# ----------------- FASTAPI -----------------
app = FastAPI(title="DevAssist AI Backend (FastAPI + LangChain)")

app.add_middleware(
    CORSMiddleware,
    allow_origins=[FRONTEND_ORIGIN] if FRONTEND_ORIGIN != "*" else ["*"],
    allow_credentials=True,
    allow_methods=["GET", "POST", "OPTIONS"],
    allow_headers=["Authorization", "Content-Type"],
)

# ----------------- PROMPT TEMPLATES -----------------
chat_template = """You are DevAssist, an AI coding assistant.

Guidelines:
- Always format responses in Markdown.
- Use section headers: Explanation:, Steps:, Fixed Code:
- Use bullet points for steps.
- Use fenced code blocks for code.
- Be friendly yet professional.

Question: {question}

Answer:
"""

stt_chat_template = """You are DevAssist, an AI coding assistant.
The input is transcribed speech. Interpret it as a developer question.
Provide clear answers with code examples.
If unclear, ask for clarification.

Spoken Question: {speech}
Answer:
"""

autodoc_template = """You are DevAssist DocBot.
Read the code and produce professional documentation in markdown.

Code: {code}
Documentation:
"""

sme_template = """
You are a senior full-stack engineer specializing in modern front-end development.
Your job is to generate **production-ready code** for websites and apps.

Guidelines:
- Always return three separate files: index.html, styles.css, and script.js
- HTML must be semantic, responsive, and mobile-first
- CSS should use Flexbox/Grid with hover/transition effects
- JavaScript must add interactivity (animations, toggles, button actions)
- Include hero, feature grid, testimonials, and footer
- Use realistic content (no lorem ipsum, no placeholders)

Prompt: {user_prompt}
Context: {context}

Output:
"""

# ----------------- CHAINS -----------------
chat_chain = PromptTemplate(input_variables=["question"], template=chat_template) | llm
stt_chain = PromptTemplate(input_variables=["speech"], template=stt_chat_template) | llm
autodoc_chain = PromptTemplate(input_variables=["code"], template=autodoc_template) | llm
sme_chain = PromptTemplate(input_variables=["user_prompt", "context"], template=sme_template) | llm

# ----------------- REQUEST MODELS -----------------
class ChatRequest(BaseModel):
    question: str

class AutoDocRequest(BaseModel):
    code: str

# ----------------- AUTH -----------------
def check_auth(authorization: str | None):
    if not PROJECT_API_KEY:
        return
    if not authorization or not authorization.startswith("Bearer "):
        raise HTTPException(status_code=401, detail="Missing bearer token")
    token = authorization.split(" ", 1)[1]
    if token != PROJECT_API_KEY:
        raise HTTPException(status_code=403, detail="Invalid token")

# ----------------- HELPER FUNCTIONS -----------------
def run_chain(chain, input_dict: dict):
    """
    Safely run a LangChain PromptTemplate | HuggingFaceEndpoint chain.
    Converts output to string and captures errors.
    """
    try:
        # Render template
        if hasattr(chain, "prompt"):
            prompt_text = chain.prompt.format(**input_dict)
        else:
            prompt_text = str(input_dict)

        # Generate using HuggingFaceEndpoint (expects str input)
        output = chain.llm.generate([{"role": "user", "content": prompt_text}])
        return output.generations[0][0].text.strip()
    except Exception:
        return {"success": False, "error": "⚠️ LLM error", "details": traceback.format_exc()}

async def process_audio(file: UploadFile, lang_hint: str | None = None):
    suffix = os.path.splitext(file.filename)[1] or ".wav"
    with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tf:
        tf.write(await file.read())
        tmp_path = tf.name

    with open(tmp_path, "rb") as f:
        audio_bytes = f.read()

    try:
        if lang_hint:
            resp = spitch_client.speech.transcribe(language=lang_hint, content=audio_bytes)
        else:
            resp = spitch_client.speech.transcribe(content=audio_bytes)
    except Exception:
        resp = spitch_client.speech.transcribe(language="en", content=audio_bytes)

    transcription = getattr(resp, "text", "") or (resp.get("text", "") if isinstance(resp, dict) else "")
    detected_lang = "en"
    try:
        detected_lang = detect(transcription) if transcription.strip() else "en"
    except Exception:
        pass

    translation = transcription
    if detected_lang != "en":
        try:
            translation_resp = spitch_client.text.translate(text=transcription, source=detected_lang, target="en")
            translation = getattr(translation_resp, "text", "") or translation_resp.get("text", "")
        except Exception:
            translation = transcription

    return transcription, detected_lang, translation

# ----------------- ENDPOINTS -----------------
@app.get("/")
def root():
    return {"status": "✅ DevAssist AI Backend running"}

@app.post("/chat")
def chat(req: ChatRequest, authorization: str | None = Header(None)):
    check_auth(authorization)
    result = run_chain(chat_chain, {"question": req.question})
    return result if isinstance(result, dict) else {"reply": result}

@app.post("/stt")
async def stt_audio(file: UploadFile = File(...), lang_hint: str | None = None, authorization: str | None = Header(None)):
    check_auth(authorization)
    transcription, detected_lang, translation = await process_audio(file, lang_hint)
    result = run_chain(stt_chain, {"speech": translation})
    return {
        "transcription": transcription,
        "detected_language": detected_lang,
        "translation": translation,
        "reply": result if isinstance(result, str) else result.get("reply", "")
    }

@app.post("/autodoc")
def autodoc(req: AutoDocRequest, authorization: str | None = Header(None)):
    check_auth(authorization)
    result = run_chain(autodoc_chain, {"code": req.code})
    return result if isinstance(result, dict) else {"documentation": result}

@app.post("/sme/generate")
async def sme_generate(payload: dict = Body(...), authorization: str | None = Header(None)):
    check_auth(authorization)
    try:
        user_prompt = payload.get("user_prompt", "")
        context_docs = retriever.get_relevant_documents(user_prompt)
        context = "\n".join([doc.page_content for doc in context_docs]) if context_docs else "No extra context"
        result = run_chain(sme_chain, {"user_prompt": user_prompt, "context": context})
        return {"success": True, "data": result if isinstance(result, str) else result.get("reply", "")}
    except Exception:
        return {"success": False, "error": "⚠️ LLM error", "details": traceback.format_exc()}

@app.post("/sme/speech-generate")
async def sme_speech_generate(file: UploadFile = File(...), lang_hint: str | None = None, authorization: str | None = Header(None)):
    check_auth(authorization)
    transcription, detected_lang, translation = await process_audio(file, lang_hint)
    try:
        context_docs = retriever.get_relevant_documents(translation)
        context = "\n".join([doc.page_content for doc in context_docs]) if context_docs else "No extra context"
        result = run_chain(sme_chain, {"user_prompt": translation, "context": context})
        return {
            "success": True,
            "transcription": transcription,
            "detected_language": detected_lang,
            "translation": translation,
            "sme_site": result if isinstance(result, str) else result.get("reply", "")
        }
    except Exception:
        return {"success": False, "error": "⚠️ LLM error", "details": traceback.format_exc()}

# ----------------- MAIN -----------------
if __name__ == "__main__":
    import uvicorn
    uvicorn.run("main:app", host="0.0.0.0", port=7860, reload=False)