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')