--- a
+++ b/docpat/views.py
@@ -0,0 +1,215 @@
+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')
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