import os
import re
import json
import requests
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from google.cloud import storage
from google.auth import exceptions
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from io import BytesIO
from dotenv import load_dotenv
import uvicorn

load_dotenv()

API_KEY = os.getenv("API_KEY")
GCS_BUCKET_NAME = os.getenv("GCS_BUCKET_NAME")
GOOGLE_APPLICATION_CREDENTIALS_JSON = os.getenv("GOOGLE_APPLICATION_CREDENTIALS_JSON")
HF_API_TOKEN = os.getenv("HF_API_TOKEN")

def validate_bucket_name(bucket_name):
    if not re.match(r"^[a-z0-9][a-z0-9\-]*[a-z0-9]$", bucket_name):
        raise ValueError(f"Invalid bucket name '{bucket_name}'. Must start and end with a letter or number.")
    return bucket_name

def validate_huggingface_repo_name(repo_name):
    if not re.match(r"^[a-zA-Z0-9_.-]+$", repo_name):
        raise ValueError(f"Invalid repository name '{repo_name}'. Must use alphanumeric characters, '-', '_', or '.'.")
    if repo_name.startswith(('-', '.')) or repo_name.endswith(('-', '.')) or '..' in repo_name:
        raise ValueError(f"Invalid repository name '{repo_name}'. Cannot start or end with '-' or '.', or contain '..'.")
    if len(repo_name) > 96:
        raise ValueError(f"Repository name '{repo_name}' exceeds max length of 96 characters.")
    return repo_name

try:
    GCS_BUCKET_NAME = validate_bucket_name(GCS_BUCKET_NAME)
    credentials_info = json.loads(GOOGLE_APPLICATION_CREDENTIALS_JSON)
    storage_client = storage.Client.from_service_account_info(credentials_info)
    bucket = storage_client.bucket(GCS_BUCKET_NAME)
except (exceptions.DefaultCredentialsError, json.JSONDecodeError, KeyError, ValueError) as e:
    raise RuntimeError(f"Error al cargar credenciales o bucket: {e}")

app = FastAPI()

class DownloadModelRequest(BaseModel):
    model_name: str
    pipeline_task: str
    input_text: str

class GCSHandler:
    def __init__(self, bucket_name):
        self.bucket = storage_client.bucket(bucket_name)

    def file_exists(self, blob_name):
        return self.bucket.blob(blob_name).exists()

    def upload_file(self, blob_name, file_stream):
        blob = self.bucket.blob(blob_name)
        blob.upload_from_file(file_stream)

    def download_file(self, blob_name):
        blob = self.bucket.blob(blob_name)
        if not blob.exists():
            raise HTTPException(status_code=404, detail=f"File '{blob_name}' not found.")
        return BytesIO(blob.download_as_bytes())

def download_model_from_huggingface(model_name):
    model_name = validate_huggingface_repo_name(model_name)
    file_patterns = [
        "pytorch_model.bin",
        "config.json",
        "tokenizer.json",
        "model.safetensors",
    ]
    for i in range(1, 100):
        file_patterns.extend([f"pytorch_model-{i:05}-of-00001", f"model-{i:05}"])
    for filename in file_patterns:
        url = f"https://huggingface.co/{model_name}/resolve/main/{filename}"
        headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
        try:
            response = requests.get(url, headers=headers, stream=True)
            if response.status_code == 200:
                blob_name = f"{model_name}/{filename}"
                bucket.blob(blob_name).upload_from_file(BytesIO(response.content))
        except Exception as e:
            raise HTTPException(status_code=500, detail=f"Error downloading {filename} from Hugging Face: {e}")

@app.post("/predict/")
async def predict(request: DownloadModelRequest):
    try:
        gcs_handler = GCSHandler(GCS_BUCKET_NAME)
        model_prefix = request.model_name
        model_files = [
            "pytorch_model.bin",
            "config.json",
            "tokenizer.json",
            "model.safetensors",
        ]
        for i in range(1, 100):
            model_files.extend([f"pytorch_model-{i:05}-of-00001", f"model-{i:05}"])
        if not any(gcs_handler.file_exists(f"{model_prefix}/{file}") for file in model_files):
            download_model_from_huggingface(model_prefix)
        model_files_streams = {file: gcs_handler.download_file(f"{model_prefix}/{file}") for file in model_files if gcs_handler.file_exists(f"{model_prefix}/{file}")}
        config_stream = model_files_streams.get("config.json")
        tokenizer_stream = model_files_streams.get("tokenizer.json")
        if not config_stream or not tokenizer_stream:
            raise HTTPException(status_code=500, detail="Required model files missing.")
        model = AutoModelForCausalLM.from_pretrained(config_stream)
        tokenizer = AutoTokenizer.from_pretrained(tokenizer_stream)
        pipeline_ = pipeline(request.pipeline_task, model=model, tokenizer=tokenizer)
        result = pipeline_(request.input_text)
        return {"response": result}
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error: {e}")

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)