from django.shortcuts import render
import pickle
import os
from django.core.files.storage import FileSystemStorage
import numpy as np
import tensorflow
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import load_img, img_to_array
import os
from django.conf import settings
from xc import predict_image # Import the function from c.py
# Path to your single image
def load_model(file_path):
if not os.path.exists(file_path):
raise FileNotFoundError(f"The model file at {file_path} does not exist.")
with open(file_path, 'rb') as file:
model = pickle.load(file)
return model
def homepage(request):
return render(request,"main.html")
def breast(request):
return render(request,"breast.html")
def faq(request):
return render(request,"faqs.html")
def heart(request):
return render(request,"heart.html")
def liver(request):
return render(request,"liver.html")
def diabetes(request):
return render(request,"diabetes.html")
def predictb(request):
if request.method == 'POST':
try:
model_path = r'breast_cancer_svm_model.pkl'
breast_model = load_model(model_path)
texture_mean = float(request.POST.get('texture_mean'))
smoothness_mean = float(request.POST.get('smoothness_mean'))
compactness_mean = float(request.POST.get('compactness_mean'))
concave_points_mean = float(request.POST.get('concave_points_mean'))
symmetry_mean = float(request.POST.get('symmetry_mean'))
fractal_dimension_mean = float(request.POST.get('fractal_dimension_mean'))
texture_se = float(request.POST.get('texture_se'))
area_se = float(request.POST.get('area_se'))
smoothness_se = float(request.POST.get('smoothness_se'))
compactness_se = float(request.POST.get('compactness_se'))
concavity_se = float(request.POST.get('concavity_se'))
concave_points_se = float(request.POST.get('concave_points_se'))
symmetry_se = float(request.POST.get('symmetry_se'))
fractal_dimension_se =float(request.POST.get('fractal_dimension_se'))
texture_worst = float(request.POST.get('texture_worst'))
area_worst = float(request.POST.get('area_worst'))
smoothness_worst = float(request.POST.get('smoothness_worst'))
compactness_worst = float(request.POST.get('compactness_worst'))
concavity_worst = float(request.POST.get('concavity_worst'))
concave_points_worst = float(request.POST.get('concave_points_worst'))
symmetry_worst = float(request.POST.get('symmetry_worst'))
fractal_dimension_worst = float(request.POST.get('fractal_dimension_worst'))
data = [ texture_mean, smoothness_mean, compactness_mean,
concave_points_mean, symmetry_mean, fractal_dimension_mean, texture_se,
area_se, smoothness_se, compactness_se, concavity_se, concave_points_se, symmetry_se,
fractal_dimension_se, texture_worst, area_worst, smoothness_worst,
compactness_worst, concavity_worst, concave_points_worst, symmetry_worst, fractal_dimension_worst]
prediction_result = breast_model.predict([data])
context = {
'prediction_result': prediction_result[0]
}
return render(request, 'predict.html', context)
except Exception as e:
return render(request, 'error.html', {'error': str(e)})
def predictd(request):
if request.method == 'POST':
try:
model_path = r'diabetes_model.pkl'
diabetes_model = load_model(model_path)
Pregnancies = float(request.POST.get('Pregnancies'))
Glucose =float(request.POST.get('Glucose'))
BloodPressure = float(request.POST.get('BloodPressure'))
SkinThickness = float(request.POST.get('SkinThickness'))
Insulin = float(request.POST.get('Insulin'))
BMI = float(request.POST.get('BMI'))
DiabetesPedigreeFunction = float(request.POST.get('DiabetesPedigreeFunction'))
Age = float(request.POST.get('Age'))
inputs = [[Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin,
BMI, DiabetesPedigreeFunction, Age
]]
prediction_result = diabetes_model.predict(inputs)
context = {
'prediction_result': prediction_result[0]
}
return render(request, 'predict.html', context)
except Exception as e:
return render(request, 'error.html', {'error': str(e)})
def predictl(request):
if request.method == 'POST':
try:
model_path = r'liver_prediction.pkl'
liver_model = load_model(model_path)
age = float(request.POST.get('Age'))
gender = float(request.POST.get('Gender'))
total_bilirubin = float(request.POST.get('Total_Bilirubin'))
direct_bilirubin = float(request.POST.get('Direct_Bilirubin'))
alkaline_phosphotase = float(request.POST.get('Alkaline_Phosphotase'))
alamine_aminotransferase = float(request.POST.get('Alamine_Aminotransferase'))
aspartate_aminotransferase = float(request.POST.get('Aspartate_Aminotransferase'))
total_proteins = float(request.POST.get('Total_Protiens'))
albumin = float(request.POST.get('Albumin'))
albumin_and_globulin_ratio = float(request.POST.get('Albumin_and_Globulin_Ratio'))
inputs = [[age, gender, total_bilirubin, direct_bilirubin, alkaline_phosphotase,
alamine_aminotransferase, aspartate_aminotransferase, total_proteins,
albumin, albumin_and_globulin_ratio]]
prediction_result =liver_model.predict(inputs)
context = {
'prediction_result': prediction_result[0]
}
return render(request, 'predict.html', context)
except Exception as e:
return render(request, 'error.html', {'error': str(e)})
def predicth(request):
if request.method == 'POST':
try:
model_path = r'heart_disease_model.pkl'
heart_model =(load_model(model_path))
age = float(request.POST.get('age'))
sex = float(request.POST.get('sex'))
cp = float(request.POST.get('cp'))
trestbps = float(request.POST.get('trestbps'))
chol = float(request.POST.get('chol'))
fbs = float(request.POST.get('fbs'))
restecg = float(request.POST.get('restecg'))
thalach = float(request.POST.get('thalach'))
exang = float(request.POST.get('exang'))
oldpeak = float(request.POST.get('oldpeak'))
slope = float(request.POST.get('slope'))
ca = float(request.POST.get('ca'))
thal = float(request.POST.get('thal'))
inputs = [[age, sex,cp,trestbps, chol, fbs, restecg,
thalach, exang, oldpeak,
slope, ca,thal]]
prediction_result = heart_model.predict(inputs)
context = {
'prediction_result': prediction_result[0]
}
return render(request, 'predict.html', context)
except Exception as e:
return render(request, 'error.html', {'error': str(e)})
def gi(request):
return render(request, 'GI_diseases.html')
# View to handle file upload and prediction
# views.py
def predictg(request):
if request.method == 'POST' and request.FILES.get('uploadedImage'):
# Handle the uploaded file
uploaded_file = request.FILES['uploadedImage']
file_name = uploaded_file.name
# Save the uploaded file temporarily
fs = FileSystemStorage()
file_path = fs.save(file_name, uploaded_file)
full_file_path = fs.path(file_path)
# Debugging: Ensure the file path is correct
print(f"File uploaded to: {full_file_path}")
# Call the predict_image function from c.py to process the image
predicted_class = predict_image(full_file_path)
if predicted_class is not None:
print(f"Predicted class: {predicted_class}") # Debugging: print the predicted class
return render(request, 'p.html', {
'predicted_class': predicted_class # Only send predicted class
})
else:
print("Prediction failed") # Debugging: print if prediction failed
return render(request, 'error.html', {'error': 'Failed to process the image or make predictions.'})
else:
return render(request, 'p.html')
def GI_Diseases_info(request):
return render(request,"gi.html")
def diabetes_info(request):
return render(request,"diabetes_info.html")
def breast_cancer_info(request):
return render(request,'breast_cancer_info.html')
def heart_disease_info(request):
return render(request,'heart_disease_info.html')
def liver_disease_info(request):
return render(request,'liver_disease_info.html')
Datasets
Models
