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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})
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