import os

import cv2
import numpy as np
import tensorflow as tf
from django.http import HttpResponse
from django.core.files.base import ContentFile
from PIL import Image
from django.conf import settings
from django.shortcuts import render
from django.urls import reverse_lazy
from django.views import View
from django.views.generic.edit import CreateView
from tensorflow.keras.models import load_model

from .models import Attendance_Label_Prediction


from django.urls import reverse_lazy

from django.urls import reverse_lazy

class PredictView(CreateView):
    template_name = 'predict_form.html'
    model = Attendance_Label_Prediction
    fields = ['image']
    success_url = reverse_lazy('prediction_result')

    def form_valid(self, form):
        model_file = os.path.join(settings.BASE_DIR, 'prediction', 'Attendify-v2.h5')
        model = load_model(model_file)

        # Get the uploaded image from the form
        image = form.instance.image
        custom_image = cv2.imdecode(np.fromstring(image.read(), np.uint8), cv2.IMREAD_GRAYSCALE)
        custom_image = cv2.resize(custom_image, (224, 224))
        custom_image = np.expand_dims(custom_image, axis=-1)
        custom_image = custom_image / 255.0

        # Convert the NumPy array to a TensorFlow tensor
        custom_image_tensor = tf.convert_to_tensor(custom_image, dtype=tf.float32)

        # Make a prediction
        predicted_probs = model.predict(np.expand_dims(custom_image, axis=0))

        # Convert predicted probabilities to class label (if using one-hot encoding)
        predicted_label = np.argmax(predicted_probs)

        # Save the predicted label along with the record
        form.instance.predicted_label = predicted_label
        form.save()

        return super().form_valid(form)


from django.http import HttpResponse
from PIL import Image

class PredictionResultView(View):
    template_name = 'prediction_result.html'

    def get(self, request):
        try:
            # Fetch the latest prediction record (you might want to adjust this logic based on your needs)
            prediction_record = Attendance_Label_Prediction.objects.latest('id')
        except Attendance_Label_Prediction.DoesNotExist:
            prediction_record = None

        if prediction_record:
            # Get the uploaded image from the record
            image_content = prediction_record.image.read()

            # Get the file extension from the upload_to attribute of the ImageField
            file_extension = prediction_record.image.name.split('.')[-1]

            # Save the image locally
            temp_image_path = os.path.join(settings.MEDIA_ROOT, f'temp_image.{file_extension}')
            with open(temp_image_path, 'wb') as temp_image_file:
                temp_image_file.write(image_content)

            # Pass the image URL, image name, and predicted label to the template
            image_url = settings.MEDIA_URL + f'temp_image.{file_extension}'
            image_name = prediction_record.image.name
            predicted_label = prediction_record.predicted_label
        else:
            image_url = None
            image_name = None
            predicted_label = None

        return render(request, self.template_name, {'image_url': image_url, 'image_name': image_name, 'predicted_label': predicted_label})