import subprocess
import sys
import torch
import base64
from io import BytesIO
from PIL import Image
import requests
from transformers import AutoModelForCausalLM, AutoProcessor
from tokenizers import Tokenizer, pre_tokenizers  
import os

def install(package):
    subprocess.check_call([sys.executable, "-m", "pip", "install", "--no-warn-script-location", package])

class EndpointHandler:
    def __init__(self, path=""):
        # Install necessary packages
        required_packages = ['timm', 'einops', 'flash-attn', 'Pillow','transformers==4.41.0.dev0']
        for package in required_packages:
            try:
                install(package)
                print(f"Successfully installed {package}")
            except Exception as e:
                print(f"Failed to install {package}: {str(e)}")

        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        print(f"Using device: {self.device}")

        # Load the model
        self.model_name = "arjunanand13/LADP_Florence-40e"
        self.model = AutoModelForCausalLM.from_pretrained(
            self.model_name, trust_remote_code=True
        ).to(self.device)

        # Manually load the tokenizer with a whitespace pre-tokenizer
        self.tokenizer = self.load_tokenizer()

        # Initialize the processor
        self.processor = AutoProcessor.from_pretrained(self.model_name, trust_remote_code=True)

        if torch.cuda.is_available():
            torch.cuda.empty_cache()

    def load_tokenizer(self):
        """Manually loads the tokenizer and adds a whitespace pre-tokenizer."""
        try:
            tokenizer = Tokenizer.from_pretrained(self.model_name)
            tokenizer.pre_tokenizer = pre_tokenizers.Whitespace()
            print("[INFO] Whitespace pre-tokenizer added.")
            return tokenizer
        except Exception as e:
            print(f"[ERROR] Failed to load tokenizer: {str(e)}")
            return None

    def process_image(self, image_data):
        """Processes image data from file path or base64-encoded string."""
        print("[DEBUG] Attempting to process image")
        try:
            if isinstance(image_data, str) and len(image_data) < 256 and os.path.exists(image_data):
                with open(image_data, 'rb') as image_file:
                    print("[DEBUG] File opened successfully")
                    image = Image.open(image_file)
            else:
                print("[DEBUG] Decoding base64 image data")
                image_bytes = base64.b64decode(image_data)
                image = Image.open(BytesIO(image_bytes))

            print("[DEBUG] Image opened:", image.format, image.size, image.mode)
            return image
        except Exception as e:
            print(f"[ERROR] Error processing image: {str(e)}")
            return None

    def __call__(self, data):
        """Processes input and generates model output."""
        try:
            inputs = data.pop("inputs", data)

            if isinstance(inputs, dict):
                image_path = inputs.get("image", None)
                text_input = inputs.get("text", "")
            else:
                image_path = inputs
                text_input = "What is in this image?"

            print("[INFO] Image path:", image_path, "| Text input:", text_input)

            image = self.process_image(image_path) if image_path else None

            model_inputs = self.processor(
                images=image if image else None,
                text=text_input,
                return_tensors="pt"
            )

            model_inputs = {k: v.to(self.device) if isinstance(v, torch.Tensor) else v
                            for k, v in model_inputs.items()}

            with torch.no_grad():
                outputs = self.model.generate(**model_inputs)

            decoded_outputs = self.processor.batch_decode(outputs, skip_special_tokens=True)
            print(f"[INFO] Generated text: {decoded_outputs[0]}")
            return {"generated_text": decoded_outputs[0]}

        except Exception as e:
            print(f"[ERROR] {str(e)}")
            return {"error": str(e)}