import os
from fastapi import FastAPI, Request, Form
from pydantic import BaseModel
from fastapi.responses import HTMLResponse
from fastapi.templating import Jinja2Templates
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from datasets import load_from_disk
import torch
import random
import subprocess
import json

# Caricamento del modello e dei dati se già scaricati
MODEL = "cardiffnlp/twitter-roberta-base-sentiment-latest"
TWEET_PROCESSED_PATH = "data/processed/tweet_eval_tokenized"
YT_PROCESSED_PATH = "data/processed/youtube_tokenized"

tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForSequenceClassification.from_pretrained(MODEL)


labels = ["negative", "neutral", "positive"]


# TWEET EVAL
if not os.path.exists(TWEET_PROCESSED_PATH):
    print(f"Dataset Tweet Eval non trovato in {TWEET_PROCESSED_PATH}. Lo genero...")

    if not os.environ.get("SKIP_DATA_PREP"):
        if not os.path.exists(TWEET_PROCESSED_PATH):
            subprocess.run(["python", "src/data_preparation.py", "tweet_eval"], check=True)
tweet_eval = load_from_disk(TWEET_PROCESSED_PATH)


# YOUTUBE COMMENTS
if not os.path.exists(YT_PROCESSED_PATH):
    print(f" Dataset YouTube non trovato in {YT_PROCESSED_PATH}. Lo genero...")
    if not os.environ.get("SKIP_DATA_PREP"):
        if not os.path.exists(YT_PROCESSED_PATH):
            subprocess.run(["python", "src/data_preparation.py", "youtube"], check=True)
youtube_ds = load_from_disk(YT_PROCESSED_PATH)

app = FastAPI(title="Sentiment Analysis API")
templates = Jinja2Templates(directory="app_templates/")


class TextInput(BaseModel):
    text: str


def predict_sentiment(text: str):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
        pred = torch.argmax(probs, dim=1).item()
        confidence = probs[0][pred].item()
    return {"label": labels[pred], "confidence": round(confidence, 3)}


@app.get("/", response_class=HTMLResponse)
async def home(request: Request):
    return templates.TemplateResponse("index.html", {"request": request})


@app.get("/random_tweet", response_class=HTMLResponse)
def random_tweet(request: Request):
    #  sample = random.choice(tweet_eval["test"])
    sample = tweet_eval["test"][random.randrange(len(tweet_eval["test"]))]
    text = (
        sample["text"]
        if "text" in sample
        else tokenizer.decode(sample["input_ids"], skip_special_tokens=True)
    )

    result = predict_sentiment(text)

    true_label = labels[sample["label"]]

    return templates.TemplateResponse(
        "random_tweet.html",
        {"request": request, "text": text, "true_label": true_label, "result": result},
    )


@app.get("/predict", response_class=HTMLResponse)
def predict_page(request: Request):
    return templates.TemplateResponse("predict.html", {"request": request, "result": None})


@app.post("/predict", response_class=HTMLResponse)
def predict_text(request: Request, text: str = Form(...)):
    result = predict_sentiment(text)
    return templates.TemplateResponse(
        "predict.html", {"request": request, "text": text, "result": result}
    )


@app.get("/random_youtube_comment", response_class=HTMLResponse)
def random_youtube_comment(request: Request):
    sample = random.choice(youtube_ds["train"])

    text = sample["text"] if "text" in sample else sample["text"]
    true_label = sample["label"] if "label" in sample else "N/A"

    if isinstance(true_label, int):

        label_map = {0: "negative", 1: "neutral", 2: "positive"}
        true_label = label_map.get(true_label, "N/A")

    result = predict_sentiment(text)

    return templates.TemplateResponse(
        "random_youtube.html",
        {"request": request, "text": text, "true_label": true_label, "result": result},
    )


@app.get("/admin", response_class=HTMLResponse)
async def admin_dashboard(request: Request):
    """Pagina principale dell'area admin."""
    metrics = None
    metrics_path = "reports/metrics.json"
    if os.path.exists(metrics_path):
        with open(metrics_path, "r") as f:
            metrics = json.load(f)
    return templates.TemplateResponse("admin.html", {"request": request, "metrics": metrics})


@app.post("/admin/train")
async def retrain_model():
    """Lancia lo script di training."""
    subprocess.run(["python", "src/train.py"], check=True)
    return {"status": "Training completato"}


@app.post("/admin/monitor")
async def run_monitoring():
    """Esegue il monitoring e aggiorna metrics.json."""
    subprocess.run(["python", "src/monitoring.py"], check=True)
    return {"status": "Monitoring completato"}


@app.get("/admin/metrics", response_class=HTMLResponse)
def view_metrics(request: Request):
    """Visualizza i risultati del monitoring in forma tabellare e grafica."""
    metrics_path = "reports/metrics.json"
    metrics = None
    if os.path.exists(metrics_path):
        with open(metrics_path, "r") as f:
            metrics = json.load(f)
    return templates.TemplateResponse("metrics.html", {"request": request, "metrics": metrics})


if __name__ == "__main__":
    import uvicorn

    uvicorn.run(app, host="0.0.0.0", port=8000)