import os
import torch
import base64
import io
import requests
import matplotlib.pyplot as plt
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForDepthEstimation
import numpy as np



class EndpointHandler:
    def __init__(self, path=""):
        # Load model and processor
        self.model_path = path or os.environ.get("MODEL_PATH", "")
        print(self.model_path)
        self.image_processor = AutoImageProcessor.from_pretrained(self.model_path)
        self.model = AutoModelForDepthEstimation.from_pretrained(self.model_path)

        # Move model to GPU if available
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model = self.model.to(self.device)

        # Set model to evaluation mode
        self.model.eval()

    def __call__(self, data):
        """
        Args:
            data: Input data in the format of a dictionary with either:
                - 'url': URL of the image
                - 'file': Base64 encoded image
                - 'image': PIL Image object
                - 'visualization': Boolean flag to return visualization-friendly format (default: False)
                - 'x': Int pixel position on axis x
                - 'y': Int pixel position on axis y
        Returns:
            Dictionary containing the depth map and metadata
        """
        # Process input data
        
        if "url" in data:
            # Download image from URL
            response = requests.get(data["url"], stream=True)
            response.raise_for_status()  # Raise an exception for HTTP errors
            image = Image.open(response.raw).convert("RGB")

        elif "file" in data:
            # Decode base64 image
            image_bytes = base64.b64decode(data["file"])
            image = Image.open(io.BytesIO(image_bytes)).convert("RGB")

        elif "image" in data:
            # Direct PIL image input
            image = data["image"]

        else:
            raise ValueError("No valid image input found. Please provide either 'url', 'file' (base64 encoded image), or 'image' (PIL Image object).")

        # Prepare image for the model
        inputs = self.image_processor(images=image, return_tensors="pt")
        inputs = {k: v.to(self.device) for k, v in inputs.items()}

        # Perform inference
        with torch.no_grad():
            outputs = self.model(**inputs)
            predicted_depth = outputs.predicted_depth

        # Interpolate to original size
        prediction = torch.nn.functional.interpolate(
            predicted_depth.unsqueeze(1),
            size=image.size[::-1],  # (height, width)
            mode="bicubic",
            align_corners=False,
        ).squeeze()

        # Convert to numpy and normalize for visualization
        depth_map = prediction.cpu().numpy()

        # Normalize depth map to 0-1 range for better visualization
        depth_min = depth_map.min()
        depth_max = depth_map.max()
        normalized_depth = (depth_map - depth_min) / (depth_max - depth_min)

        # Check if visualization is requested
        visualization = data.get("visualization", False)

        # Check the pixels to return  if no pixel provided will return the [0,0] position
        x= data.get('x',0)
        y= data.get('y',0)

        map = np.array(depth_map)
        print(map.shape)

        if visualization:
            # Convert depth map to a visualization-friendly format (grayscale image)
            # Create a figure and plot the depth map
            plt.figure(figsize=(10, 10))
            plt.imshow(normalized_depth, cmap='plasma')
            plt.axis('off')

            # Save the figure to a BytesIO object
            buf = io.BytesIO()
            plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0)
            plt.close()
            buf.seek(0)

            # Convert to base64 for easy transmission
            img_str = base64.b64encode(buf.getvalue()).decode('utf-8')

            result = {
                "visualization": img_str,
                "min_depth": float(depth_min),
                "max_depth": float(depth_max),
                "format": "base64_png"
            }
        else:
            result = {
                "deph": depth_map[y][x]
                # "depth": normalized_depth.tolist(),
                # "depth": compressed_depth_base64,
                # "depth_map": depth_map,
                # "min_depth": float(depth_min),
                # "max_depth": float(depth_max),
                # "shape": list(normalized_depth.shape)
            }

        return result