import io
import pickle
import numpy as np
import torch
from fastapi import FastAPI, UploadFile, File, Form
from fastapi.middleware.cors import CORSMiddleware
from PIL import Image
from transformers import AutoTokenizer, AutoModel
import open_clip
import re

device = "cuda" if torch.cuda.is_available() else "cpu"

TEXT_MODEL_NAME = "indobenchmark/indobert-large-p1"
tokenizer = AutoTokenizer.from_pretrained(TEXT_MODEL_NAME)
text_model = AutoModel.from_pretrained(TEXT_MODEL_NAME).to(device)
text_model.eval()

clip_model, _, clip_preprocess = open_clip.create_model_and_transforms(
    "EVA01-g-14-plus",
    pretrained="merged2b_s11b_b114k"
)
clip_model.to(device)
clip_model.eval()

with open("xgb_full.pkl", "rb") as f:
    xgb_model = pickle.load(f)

def preprocess_text(text: str) -> str:
    text = str(text).lower()                                      
    text = re.sub(r'http\S+|www\.\S+', '', text)                  
    text = re.sub(r'@\w+|#\w+', '', text)                         
    text = re.sub(r'[^a-z\s]', ' ', text)                        
    text = re.sub(r'\s+', ' ', text).strip()
    return " ".join(text.split())

app = FastAPI(
    title="Multimodal Water Pollution Risk API",
    description=(
        "Input: text + image + geospatial + time\n"
        "Model: IndoBERT + EVA-CLIP (HF Hub) + XGBoost (xgb.pkl)\n"
    ),
    version="1.0.0",
)

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

@app.get("/")
def root():
    return {
        "status": "OK",
        "message": "Multimodal Water Pollution Risk API is running.",
        "info": "Use POST /predict with text, image, and features.",
    }

@app.post("/predict")
async def predict(
    text: str = Form(...),
    longitude: float = Form(...),
    latitude: float = Form(...),
    location_cluster: int = Form(...),
    hour: int = Form(...),
    dayofweek: int = Form(...),
    month: int = Form(...),
    image: UploadFile = File(...),
):
    # 1. preprocess text
    cleaned_text = preprocess_text(text)

    # 2. encode text (ambil CLS token-nya)
    text_inputs = tokenizer(
        cleaned_text,
        return_tensors="pt",
        padding="max_length",
        truncation=True,
        max_length=128,
    )
    text_inputs = {k: v.to(device) for k, v in text_inputs.items()}
    with torch.no_grad():
        text_emb = text_model(**text_inputs).last_hidden_state[:, 0, :] # take the CLS token only
    text_emb = text_emb.cpu().numpy()

    # 3. encode image (EVA-CLIP image embedding)
    img_bytes = await image.read()
    pil_img = Image.open(io.BytesIO(img_bytes)).convert("RGB")
    img_tensor = clip_preprocess(pil_img).unsqueeze(0).to(device)

    with torch.no_grad():
        img_emb = clip_model.encode_image(img_tensor)
    img_emb = img_emb.cpu().numpy()

    # 4. additional numeric features (longitude, latitude, location_cluster, hour, dayofweek, month)
    add_feats = np.array(
        [[longitude, latitude, location_cluster, hour, dayofweek, month]],
        dtype=np.float32,
    )

    # 5. concatenate (early fusion): [image_emb, text_emb, add_feats]
    fused = np.concatenate([img_emb, text_emb, add_feats], axis=1)

    # 6. predict
    proba = xgb_model.predict_proba(fused)[0]
    pred_idx = int(np.argmax(proba))
    label = "KRITIS" if pred_idx == 1 else "WASPADA"

    return {
        "prediction": label,
        "probabilities": {
            "WASPADA": float(proba[0]),
            "KRITIS": float(proba[1]),
        },
    }

if __name__ == "__main__":
    import uvicorn

    uvicorn.run(app, host="0.0.0.0", port=7860)