import base64
from io import BytesIO
import io
import os
import sys
import cv2
from matplotlib import pyplot as plt
import numpy as np
import torch
import tempfile
from PIL import Image
from torchvision.transforms.functional import to_pil_image
from torchvision import transforms
from PIL import ImageOps
import os.path as osp

from torchcam.methods import CAM
from torchcam import methods as torchcam_methods
from torchcam.utils import overlay_mask

root_path = osp.abspath(osp.join(__file__, osp.pardir))
sys.path.append(root_path)

from preprocessing.dataset_creation import EyeDentityDatasetCreation
from utils import get_model

CAM_METHODS = ["CAM"]


@torch.no_grad()
def load_model(model_configs, device="cpu"):
    """Loads the pre-trained model."""
    model_path = os.path.join(root_path, model_configs["model_path"])
    model_dict = torch.load(model_path, map_location=device)
    model = get_model(model_configs=model_configs)
    model.load_state_dict(model_dict)
    model = model.to(device).eval()
    return model


def extract_frames(video_path):
    """Extracts frames from a video file."""
    import os

    # Debug: Check if file exists and get info
    print(f"🔍 DEBUG: Attempting to extract frames from: {video_path}")
    print(f"🔍 DEBUG: File exists: {os.path.exists(video_path)}")

    if os.path.exists(video_path):
        file_size = os.path.getsize(video_path)
        print(f"🔍 DEBUG: File size: {file_size} bytes")
        print(f"🔍 DEBUG: File permissions: {oct(os.stat(video_path).st_mode)}")
    else:
        print(f"❌ DEBUG: File does not exist at path: {video_path}")
        return []

    # Debug: Try to open with OpenCV
    print(f"🔍 DEBUG: Creating VideoCapture object...")
    vidcap = cv2.VideoCapture(video_path)

    # Debug: Check if VideoCapture opened successfully
    is_opened = vidcap.isOpened()
    print(f"🔍 DEBUG: VideoCapture opened successfully: {is_opened}")

    if not is_opened:
        print(f"❌ DEBUG: Failed to open video with OpenCV")
        vidcap.release()
        return []

    # Debug: Get video properties
    frame_count = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
    fps = vidcap.get(cv2.CAP_PROP_FPS)
    width = int(vidcap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(vidcap.get(cv2.CAP_PROP_FRAME_HEIGHT))

    print(f"🔍 DEBUG: Video properties - Frames: {frame_count}, FPS: {fps}, Size: {width}x{height}")

    frames = []
    frame_index = 0
    success, image = vidcap.read()

    print(f"🔍 DEBUG: First frame read success: {success}")

    while success:
        image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        frames.append(image_rgb)
        success, image = vidcap.read()
        frame_index += 1

        # Debug: Log progress every 10 frames
        if frame_index % 10 == 0:
            print(f"🔍 DEBUG: Extracted {frame_index} frames so far...")

    vidcap.release()

    print(f"✅ DEBUG: Successfully extracted {len(frames)} frames from video")
    return frames


def resize_frame(frame, max_width=640, max_height=480):
    """Resizes a frame while maintaining aspect ratio."""
    if isinstance(frame, np.ndarray):
        frame = Image.fromarray(frame)
    
    # Calculate the scaling factor
    width, height = frame.size
    scale_w = max_width / width
    scale_h = max_height / height
    scale = min(scale_w, scale_h)
    
    # Resize the frame
    new_width = int(width * scale)
    new_height = int(height * scale)
    return frame.resize((new_width, new_height), Image.Resampling.LANCZOS)


def is_image(file_extension):
    """Check if file extension is an image format."""
    return file_extension.lower() in ["png", "jpg", "jpeg", "bmp", "tiff", "webp"]


def is_video(file_extension):
    """Check if file extension is a video format."""
    return file_extension.lower() in ["mp4", "avi", "mov", "mkv", "webm", "flv", "wmv"]


def get_configs(blink_detection=False):
    """Get configuration for feature extraction."""
    upscale = "-"
    upscale_method_or_model = "-"
    if upscale == "-":
        sr_configs = None
    else:
        sr_configs = {
            "method": upscale_method_or_model,
            "params": {"upscale": upscale},
        }
    config_file = {
        "sr_configs": sr_configs,
        "feature_extraction_configs": {
            "blink_detection": blink_detection,
            "upscale": upscale,
            "extraction_library": "mediapipe",
        },
    }
    return config_file


def setup_gradio(pupil_selection, tv_model):
    """Setup models and data structures for Gradio processing."""
    left_pupil_model = None
    left_pupil_cam_extractor = None
    right_pupil_model = None
    right_pupil_cam_extractor = None
    output_frames = {}
    input_frames = {}
    predicted_diameters = {}

    if pupil_selection == "both":
        selected_eyes = ["left_eye", "right_eye"]
    elif pupil_selection == "left_pupil":
        selected_eyes = ["left_eye"]
    elif pupil_selection == "right_pupil":
        selected_eyes = ["right_eye"]

    for eye_type in selected_eyes:
        model_configs = {
            "model_path": root_path + f"/pre_trained_models/{tv_model}/{eye_type}.pt",
            "registered_model_name": tv_model,
            "num_classes": 1,
        }
        if eye_type == "left_eye":
            left_pupil_model = load_model(model_configs)
            left_pupil_cam_extractor = None
        else:
            right_pupil_model = load_model(model_configs)
            right_pupil_cam_extractor = None
        
        output_frames[eye_type] = []
        input_frames[eye_type] = []
        predicted_diameters[eye_type] = []

    return (
        selected_eyes,
        input_frames,
        output_frames,
        predicted_diameters,
        left_pupil_model,
        left_pupil_cam_extractor,
        right_pupil_model,
        right_pupil_cam_extractor,
    )


def process_frames_gradio(input_imgs, tv_model, pupil_selection, blink_detection=False):
    """
    Process frames without Streamlit dependencies.
    """
    try:
        config_file = get_configs(blink_detection)

        (
            selected_eyes,
            input_frames,
            output_frames,
            predicted_diameters,
            left_pupil_model,
            left_pupil_cam_extractor,
            right_pupil_model,
            right_pupil_cam_extractor,
        ) = setup_gradio(pupil_selection, tv_model)

        ds_creation = EyeDentityDatasetCreation(
            feature_extraction_configs=config_file["feature_extraction_configs"],
            sr_configs=config_file["sr_configs"],
        )
    except Exception as e:
        print(f"Error in setup: {e}")
        # Return empty results if setup fails
        return {}, {}, {}

    preprocess_steps = [
        transforms.Resize(
            [32, 64],
            interpolation=transforms.InterpolationMode.BICUBIC,
            antialias=True,
        ),
        transforms.ToTensor(),
    ]
    preprocess_function = transforms.Compose(preprocess_steps)

    for idx, input_img in enumerate(input_imgs):
        try:
            img = np.array(input_img)
            ds_results = ds_creation(img)
        except Exception as e:
            print(f"Error in MediaPipe processing for frame {idx}: {e}")
            ds_results = None

        left_eye = None
        right_eye = None
        blinked = False

        if ds_results is not None and "face" in ds_results:
            has_face = True
        else:
            has_face = False

        if has_face and ds_results is not None:
            if blink_detection and "blinks" in ds_results:
                blinked = ds_results["blinks"]["blinked"]

            if not blinked and "eyes" in ds_results:
                if "left_eye" in ds_results["eyes"] and ds_results["eyes"]["left_eye"] is not None:
                    left_eye_img = to_pil_image(ds_results["eyes"]["left_eye"])
                    input_img_tensor = preprocess_function(left_eye_img)
                    input_img_tensor = input_img_tensor.unsqueeze(0)
                    if pupil_selection in ["left_pupil", "both"]:
                        left_eye = input_img_tensor

                if "right_eye" in ds_results["eyes"] and ds_results["eyes"]["right_eye"] is not None:
                    right_eye_img = to_pil_image(ds_results["eyes"]["right_eye"])
                    input_img_tensor = preprocess_function(right_eye_img)
                    input_img_tensor = input_img_tensor.unsqueeze(0)
                    if pupil_selection in ["right_pupil", "both"]:
                        right_eye = input_img_tensor

        for eye_type in selected_eyes:
            if blinked:
                if left_eye is not None and eye_type == "left_eye":
                    _, height, width = left_eye.squeeze(0).shape
                    input_image_pil = to_pil_image(left_eye.squeeze(0))
                elif right_eye is not None and eye_type == "right_eye":
                    _, height, width = right_eye.squeeze(0).shape
                    input_image_pil = to_pil_image(right_eye.squeeze(0))
                else:
                    # Create a default black image if no eye detected
                    input_image_pil = Image.new('RGB', (64, 32), 'black')
                    height, width = 32, 64

                input_img_np = np.array(input_image_pil)
                zeros_img = to_pil_image(np.zeros((height, width, 3), dtype=np.uint8))
                output_img_np = np.array(zeros_img)
                predicted_diameter = "blink"
            else:
                if left_eye is not None and eye_type == "left_eye":
                    if left_pupil_cam_extractor is None:
                        if tv_model == "ResNet18":
                            target_layer = left_pupil_model.resnet.layer4[-1].conv2
                        elif tv_model == "ResNet50":
                            target_layer = left_pupil_model.resnet.layer4[-1].conv3
                        else:
                            raise Exception(f"No target layer available for selected model: {tv_model}")
                        left_pupil_cam_extractor = torchcam_methods.__dict__["CAM"](
                            left_pupil_model,
                            target_layer=target_layer,
                            fc_layer=left_pupil_model.resnet.fc,
                            input_shape=left_eye.shape,
                        )
                    output = left_pupil_model(left_eye)
                    predicted_diameter = output[0].item()
                    act_maps = left_pupil_cam_extractor(0, output)
                    activation_map = act_maps[0] if len(act_maps) == 1 else left_pupil_cam_extractor.fuse_cams(act_maps)
                    input_image_pil = to_pil_image(left_eye.squeeze(0))
                elif right_eye is not None and eye_type == "right_eye":
                    if right_pupil_cam_extractor is None:
                        if tv_model == "ResNet18":
                            target_layer = right_pupil_model.resnet.layer4[-1].conv2
                        elif tv_model == "ResNet50":
                            target_layer = right_pupil_model.resnet.layer4[-1].conv3
                        else:
                            raise Exception(f"No target layer available for selected model: {tv_model}")
                        right_pupil_cam_extractor = torchcam_methods.__dict__["CAM"](
                            right_pupil_model,
                            target_layer=target_layer,
                            fc_layer=right_pupil_model.resnet.fc,
                            input_shape=right_eye.shape,
                        )
                    output = right_pupil_model(right_eye)
                    predicted_diameter = output[0].item()
                    act_maps = right_pupil_cam_extractor(0, output)
                    activation_map = (
                        act_maps[0] if len(act_maps) == 1 else right_pupil_cam_extractor.fuse_cams(act_maps)
                    )
                    input_image_pil = to_pil_image(right_eye.squeeze(0))
                else:
                    # No eye detected, create default values
                    input_image_pil = Image.new('RGB', (64, 32), 'black')
                    predicted_diameter = "no_eye_detected"
                    output_img_np = np.array(input_image_pil)
                    input_frames[eye_type].append(np.array(input_image_pil))
                    output_frames[eye_type].append(output_img_np)
                    predicted_diameters[eye_type].append(predicted_diameter)
                    continue

                # Create CAM overlay
                activation_map_pil = to_pil_image(activation_map, mode="F")
                result = overlay_mask(input_image_pil, activation_map_pil, alpha=0.5)
                input_img_np = np.array(input_image_pil)
                output_img_np = np.array(result)

            input_frames[eye_type].append(input_img_np)
            output_frames[eye_type].append(output_img_np)
            predicted_diameters[eye_type].append(predicted_diameter)

    return input_frames, output_frames, predicted_diameters