from fastapi import FastAPI, File, Form, UploadFile
from fastapi.responses import JSONResponse, Response
from concrete.ml.deployment import FHEModelServer
import numpy as np

from concrete.ml.deployment import FHEModelClient
import subprocess
from pathlib import Path


from utils import (
    CLIENT_DIR,
    CURRENT_DIR,
    DEPLOYMENT_DIR,
    SERVER_DIR,
    INPUT_BROWSER_LIMIT,
    KEYS_DIR,
    SERVER_URL,
    TARGET_COLUMNS,
    TRAINING_FILENAME,
    clean_directory,
    get_disease_name,
    load_data,
    pretty_print,
)


import time
from typing import List

# Load the FHE server
# FHE_SERVER = FHEModelServer(DEPLOYMENT_DIR)


app = FastAPI()

@app.get("/")
def greet_json():
    return {"Hello": "World!"}


def root():
    """
    Root endpoint of the health prediction API.

    Returns:
        dict: The welcome message.
    """
    return {"message": "Welcome to your disease prediction with FHE!"}


@app.post("/send_input")
def send_input(
    user_id: str = Form(),
    files: List[UploadFile] = File(),
):
    """Send the inputs to the server."""

    print("\nSend the data to the server ............\n")

    # Receive the Client's files (Evaluation key + Encrypted symptoms)
    evaluation_key_path = SERVER_DIR / f"{user_id}_valuation_key"
    encrypted_input_path = SERVER_DIR / f"{user_id}_encrypted_input"

    # Save the files using the above paths
    with encrypted_input_path.open("wb") as encrypted_input, evaluation_key_path.open(
        "wb"
    ) as evaluation_key:
        encrypted_input.write(files[0].file.read())
        evaluation_key.write(files[1].file.read())


@app.post("/run_fhe")
def run_fhe(
    user_id: str = Form(),
):
    """Inference in FHE."""

    print("\nRun in FHE in the server ............\n")
    evaluation_key_path = SERVER_DIR / f"{user_id}_valuation_key"
    encrypted_input_path = SERVER_DIR / f"{user_id}_encrypted_input"

    # Read the files (Evaluation key + Encrypted symptoms) using the above paths
    with encrypted_input_path.open("rb") as encrypted_output_file, evaluation_key_path.open(
        "rb"
    ) as evaluation_key_file:
        encrypted_output = encrypted_output_file.read()
        evaluation_key = evaluation_key_file.read()

    # Run the FHE execution
    start = time.time()
    encrypted_output = FHE_SERVER.run(encrypted_output, evaluation_key)
    assert isinstance(encrypted_output, bytes)
    fhe_execution_time = round(time.time() - start, 2)

    # Retrieve the encrypted output path
    encrypted_output_path = SERVER_DIR / f"{user_id}_encrypted_output"

    # Write the file using the above path
    with encrypted_output_path.open("wb") as f:
        f.write(encrypted_output)

    return JSONResponse(content=fhe_execution_time)


@app.post("/get_output")
def get_output(user_id: str = Form()):
    """Retrieve the encrypted output from the server."""

    print("\nGet the output from the server ............\n")

    # Path where the encrypted output is saved
    encrypted_output_path = SERVER_DIR / f"{user_id}_encrypted_output"

    # Read the file using the above path
    with encrypted_output_path.open("rb") as f:
        encrypted_output = f.read()

    time.sleep(1)

    # Send the encrypted output
    return Response(encrypted_output)


@app.post("/generate_keys")
def generate_keys(user_symptoms: List[str]):
    """
    Endpoint to generate keys based on user symptoms.
    
    Args:
        user_symptoms (List[str]): The list of user symptoms.

    Returns:
        JSONResponse: A response containing the generated keys and user ID.
    """
    def is_none(obj):
        return obj is None or (obj is not None and len(obj) == 0)

    # Call the key generation function
    clean_directory()

    if is_none(user_symptoms):
        return JSONResponse(
            status_code=400, content={"error": "Please submit your symptoms first."}
        )

    # Generate a random user ID
    user_id = np.random.randint(0, 2**32)
    print(f"Your user ID is: {user_id}....")

    client = FHEModelClient(path_dir=DEPLOYMENT_DIR, key_dir=KEYS_DIR / f"{user_id}")
    client.load()

    # Creates the private and evaluation keys on the client side
    client.generate_private_and_evaluation_keys()

    # Get the serialized evaluation keys
    serialized_evaluation_keys = client.get_serialized_evaluation_keys()
    assert isinstance(serialized_evaluation_keys, bytes)

    # Save the evaluation key
    evaluation_key_path = KEYS_DIR / f"{user_id}/evaluation_key"
    with evaluation_key_path.open("wb") as f:
        f.write(serialized_evaluation_keys)

    serialized_evaluation_keys_shorten_hex = serialized_evaluation_keys.hex()[:INPUT_BROWSER_LIMIT]

    return JSONResponse(
        content={
            "user_id": user_id,
            "evaluation_key": serialized_evaluation_keys_shorten_hex,
            "evaluation_key_size": f"{len(serialized_evaluation_keys) / (10**6):.2f} MB"
        }
    )


@app.post("/run_dev")
def run_dev_script():
    """
    Endpoint to execute the dev.py script to generate deployment files.
    
    Returns:
        JSONResponse: Success message or error details.
    """
    try:
        # Define the path to dev.py
        dev_script_path = Path(__file__).parent / "dev.py"

        # Execute the dev.py script
        result = subprocess.run(
            ["python", str(dev_script_path)],
            capture_output=True,
            text=True,
            check=True
        )

        # Return success message with output
        return JSONResponse(
            content={"message": "dev.py executed successfully!", "output": result.stdout}
        )

    except subprocess.CalledProcessError as e:
        # Return error message in case of failure
        return JSONResponse(
            status_code=500,
            content={"error": "Failed to execute dev.py", "details": e.stderr}
        )