import os
import io
from io import BytesIO
from fastapi import FastAPI, UploadFile, File, Form
from fastapi.responses import JSONResponse, HTMLResponse
from fastapi.middleware.cors import CORSMiddleware
from fastapi.staticfiles import StaticFiles
from huggingface_hub import InferenceClient, login
from PyPDF2 import PdfReader
from docx import Document
from PIL import Image
from routers import ai   # conservez vos routes annexes

# ──────────────────────────────────────────────────────────────────────────────
# 1) Authentification Hugging Face
# ──────────────────────────────────────────────────────────────────────────────
HF_TOKEN = os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACE_HUB_TOKEN")
if not HF_TOKEN:
    raise RuntimeError(
        "Variable d'environnement HF_TOKEN absente ; créez un jeton « Read » "
        "sur https://huggingface.co/settings/tokens et exportez-le (voir .env)."
    )

login(token=HF_TOKEN)           # Authentifie tout le process

# ──────────────────────────────────────────────────────────────────────────────
# 2) Configuration FastAPI
# ──────────────────────────────────────────────────────────────────────────────
PORT = int(os.getenv("PORT", 7860))

app = FastAPI(
    title="AI Web App API",
    description="Backend API for AI-powered web application",
    version="1.0.0"
)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

app.mount("/", StaticFiles(directory=".", html=True), name="static")
app.include_router(ai.router)

# Clients HF (token passé implicitement)
summary_client       = InferenceClient("facebook/bart-large-cnn")
qa_client            = InferenceClient("deepset/roberta-base-squad2")
image_caption_client = InferenceClient("nlpconnect/vit-gpt2-image-captioning")

# ──────────────────────────────────────────────────────────────────────────────
# 3) Utils : extraction texte, routes API (inchangés ou presque)
# ──────────────────────────────────────────────────────────────────────────────
def extract_text_from_pdf(content: bytes) -> str:
    reader = PdfReader(io.BytesIO(content))
    return "\n".join(p.extract_text() or "" for p in reader.pages).strip()

def extract_text_from_docx(content: bytes) -> str:
    doc = Document(io.BytesIO(content))
    return "\n".join(p.text for p in doc.paragraphs).strip()

def process_uploaded_file(file: UploadFile) -> str:
    content = file.file.read()
    ext = file.filename.rsplit(".", 1)[-1].lower()
    if ext == "pdf":
        return extract_text_from_pdf(content)
    if ext == "docx":
        return extract_text_from_docx(content)
    if ext == "txt":
        return content.decode("utf-8").strip()
    raise ValueError("Type de fichier non supporté")

# … (gardez vos trois routes /analyze, /ask, /interpret_image identiques)

@app.get("/api/health")
async def health_check():
    return {"status": "healthy", "version": "1.0.0", "hf_token_set": True}

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=PORT)