routes

routes.py

+from flask import render_template, request
+from app import app
+import numpy as np  # linear algebra
+import pandas as pd  # data processing, CSV file I/O (e.g. pd.read_csv)
+import os
+import cv2
+from pathlib import Path
+import pickle
+import tensorflow as tf
+import sklearn
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+
+#### Home Page ####
+
+
+@app.route('/')
+@app.route('/home')
+def home():
+    return render_template('home.html')
+
+#### Lung Cancer Disease ####
+
+
+@app.route('/lungcancer')
+def lungcancer():
+    return render_template('lungCancer.html')
+
+
+@app.route('/lungpradict')
+def lungpradict():
+    return render_template('lung_cancer_detection.html')
+
+
+@app.route('/predictLungCancer', methods=['POST'])
+def predictLungCancer():
+    GENDER = request.form['GENDER']
+    AGE = request.form['AGE']
+    SMOKING = request.form['SMOKING']
+    YELLOW_FINGERS = request.form['YELLOW_FINGERS']
+    ANXIETY = request.form['ANXIETY']
+    PEER_PRESSURE = request.form['PEER_PRESSURE']
+    CHRONICDISEASE = request.form['CHRONICDISEASE']
+    FATIGUE = request.form['FATIGUE']
+    ALLERGY = request.form['ALLERGY']
+    WHEEZING = request.form['WHEEZING']
+    ALCOHOL = request.form['ALCOHOL']
+    COUGHING = request.form['COUGHING']
+    SHORTBREATH = request.form['SHORTBREATH']
+    SWALLOW = request.form['SWALLOW']
+    CHESTPAIN = request.form['CHESTPAIN']
+
+    with open('models/finalized_model_lung_classification_LR.sav', 'rb') as f:
+        model = pickle.load(f)
+
+    input_data = (GENDER, AGE, SMOKING, YELLOW_FINGERS, ANXIETY, PEER_PRESSURE, CHRONICDISEASE,
+                  FATIGUE, ALLERGY, WHEEZING, ALCOHOL, COUGHING, SHORTBREATH, SWALLOW, CHESTPAIN)
+    input_data3 = np.array(input_data).astype(int)
+
+    input_data_as_numpy_array = np.asarray(input_data3)
+
+    input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)
+    prediction = model.predict(input_data_reshaped)
+
+    if (prediction == 1):
+        result = "You have a lung disease"
+        disease = 'lung'
+
+        return docRec(disease, result, value="lung")
+
+    else:
+        result = "You don't have a lung disease"
+        return render_template('results.html', result=result)
+
+
+@app.route('/lungcancerlevel')
+def lungcancerlevel():
+    return render_template('lung_cancer_level_detection.html')
+
+
+@app.route('/predictLungCancerLevel', methods=['POST'])
+def predictLungCancerLevel():
+    GENDER = request.form['GENDER']
+    AGE = request.form['AGE']
+    SMOKING = request.form['SMOKING']
+    FATIGUE = request.form['FATIGUE']
+    ALLERGY = request.form['ALLERGY']
+    WHEEZING = request.form['WHEEZING']
+    ALCOHOL = request.form['ALCOHOL']
+    COUGHING = request.form['COUGHING']
+    SHORTBREATH = request.form['SHORTBREATH']
+    SWALLOW = request.form['SWALLOW']
+    CHESTPAIN = request.form['CHESTPAIN']
+    GENETICRISK = request.form['GENETICRISK']
+    COUGHINGBLOOD = request.form['COUGHINGBLOOD']
+
+    with open('models/finalized_model_lung_cancer_LR_new_level.sav', 'rb') as f:
+        model = pickle.load(f)
+
+    input_data = (AGE, GENDER, ALCOHOL, ALLERGY, GENETICRISK, SMOKING, CHESTPAIN,
+                  COUGHINGBLOOD, FATIGUE, SHORTBREATH, WHEEZING, SWALLOW, COUGHING)
+
+    # input_data2 = (1, 69, 0, 1, 0, 1, 1, 1, 1, 1)
+    # change input data to numpy array
+    input_data_as_numpy_array = np.asarray(input_data)
+
+    # reshape the numpy array as we are predicting for only on instance
+
+    input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)
+    prediction = model.predict(input_data_reshaped)
+
+    if (prediction == ['1']):
+        result = "Your risk is low"
+    elif (prediction == ['2']):
+        result = "Your risk is medium"
+    else:
+        result = "Your risk is high"
+
+    disease = 'lung'
+    return docRec(disease, result)
+
+
+@app.route('/lungxray')
+def lungxray():
+    return render_template('lungXray.html')
+
+
+def predictxray(img):
+    model = tf.keras.models.load_model('models/lung_disease_model.h5')
+
+    pic = []
+    img = cv2.resize(img, (224, 224))
+    if img.shape[2] == 1:
+        img = np.dstack([img, img, img])
+    img = np.array(img)
+    img = img/255
+    # label = to_categorical(0, num_classes=2)
+    pic.append(img)
+    # pic_labels.append(pneu)
+    pic1 = np.array(pic)
+    a = model.predict(pic1)
+    print(a.argmax())
+    predicts = a.argmax()
+    predicts
+
+    if predicts == 0:
+        return "Anatomia Normal"
+    elif predicts == 1:
+        return "Inflammatory processes (pneumonia)"
+    elif predicts == 2:
+        return "Higher density (pleural effusion, atelectatic consolidation, hydrothorax, empyema)"
+    elif predicts == 3:
+        return "Lower density (pneumotorax, pneumomediastinum, pneumoperitoneum)"
+    elif predicts == 4:
+        return "Obstructive pulmonary diseases (emphysema, bronchopneumonia, bronchiectasis, embolism)"
+    elif predicts == 5:
+        return "Degenerative infectious diseases (tuberculosis , sarcoidosis, proteinosis, fibrosis)"
+    elif predicts == 6:
+        return "Encapsulated lesions (abscesses, nodules, cysts, tumor masses, metastases)"
+    elif predicts == 7:
+        return "Mediastinal changes (pericarditis, arteriovenous malformations, lymph node enlargement)"
+    else:
+        return "Chest changes (atelectasis, malformations, agenesis, hypoplasia)"
+
+
+@app.route('/process_image_lung', methods=['POST'])
+def process_image_lung():
+    if request.method == 'POST':
+        file = request.files['file']
+        img_bytes = file.read()  # Read image data as bytes
+        # Convert bytes to OpenCV image
+        img = cv2.imdecode(np.frombuffer(img_bytes, np.uint8), -1)
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # Convert BGR to RGB
+        result = predictxray(img)  # Make prediction using img
+        disease = 'lung'
+        return docRec(disease, result)
+
+    return 'File uploaded successfully!'
+
+######################### Kidney Disease #########################################
+
+
+@app.route('/predictKidneypage')
+def predictKidneypage():
+    return render_template('kidney_prediction.html')
+
+
+@app.route('/kidney')
+def kidney():
+    return render_template('kidney.html')
+
+
+@app.route('/predictKidney', methods=['POST'])
+def predictKidney():
+
+    Bp = request.form['bp']
+    Sg = request.form['sg']
+    Rbc = request.form['rbc']
+    Bu = request.form['bu']
+    Sc = request.form['sc']
+    Sod = request.form['sod']
+    Pot = request.form['pot']
+    Hemo = request.form['hemo']
+    Wbcc = request.form['wbcc']
+    Rbcc = request.form['rbcc']
+    Htn = request.form['htn']
+
+    with open('models/finalized_model_kidney_prediction_RF.sav', 'rb') as f:
+        model = pickle.load(f)
+
+    input_data = (Bp, Sg, Rbc, Bu, Sc, Sod, Pot, Hemo, Wbcc, Rbcc, Htn)
+    # input_data3 = np.array(input_data).astype(int)
+
+    input_data_as_numpy_array = np.asarray(input_data)
+
+    input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)
+    prediction = model.predict(input_data_reshaped)
+    print(input_data)
+    if (prediction == 1):
+        disease = "kidney"
+        result = "You have a chronic kidney disease"
+        return docRec(disease, result, value="kidney")
+    else:
+        result = "You don't have a chronic kidney disease"
+
+        return render_template('results.html', result=result)
+
+
+@app.route('/kidneyDisease')
+def kidneyDisease():
+    return render_template('kidney_detection.html')
+
+
+def predict(img):
+    model = tf.keras.models.load_model('models/kidney_detect_model.h5')
+
+    pic = []
+    img = cv2.resize(img, (28, 28))
+    if img.shape[2] == 1:
+        img = np.dstack([img, img, img])
+    img = np.array(img)
+    img = img/255
+    # label = to_categorical(0, num_classes=2)
+    pic.append(img)
+    # pic_labels.append(pneu)
+    pic1 = np.array(pic)
+    a = model.predict(pic1)
+    print(a.argmax())
+    predicts = a.argmax()
+    predicts
+
+    if predicts == 0:
+        return "There is a cyst in the uploaded picture. You are not well"
+    elif predicts == 1:
+        return "There is nothing unnatural in the uploaded image. you are fine"
+    elif predicts == 2:
+        return "There is a stone in the uploaded picture. You are not well"
+    else:
+        return "There is a Tumor in the uploaded picture. You are not well"
+
+
+@app.route('/process_image_kidney', methods=['POST'])
+def process_image_kidney():
+    if request.method == 'POST':
+        file = request.files['file']
+        img_bytes = file.read()  # Read image data as bytes
+        # Convert bytes to OpenCV image
+        img = cv2.imdecode(np.frombuffer(img_bytes, np.uint8), -1)
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # Convert BGR to RGB
+        prediction = predict(img)  # Make prediction using img
+        disease = "kidney"
+        return docRec(disease, result=prediction, value="kidney")
+
+    return 'File uploaded successfully!'
+
+################################ Skin Cancer ##########################################
+
+
+@app.route('/skinCancerPage')
+def skinCancerPage():
+    return render_template('skinCancerPage.html')
+
+
+@app.route('/skincancer')
+def skincancer():
+    return render_template('skincancer.html')
+
+
+def predict2(img):
+    model = tf.keras.models.load_model('models/skinmodel.h5')
+
+    pic = []
+    img = cv2.resize(img, (224, 224))
+    if img.shape[2] == 1:
+        img = np.dstack([img, img, img])
+    img = np.array(img)
+    img = img/255
+    # label = to_categorical(0, num_classes=2)
+    pic.append(img)
+    # pic_labels.append(pneu)
+    pic1 = np.array(pic)
+    a = model.predict(pic1)
+    print(a.argmax())
+    predicts = a.argmax()
+    predicts
+
+    if predicts == 0:
+        return "Melanocytic nevi"
+    elif predicts == 1:
+        return "Melanoma"
+    elif predicts == 2:
+        return "Benign keratosis-like lesions"
+    elif predicts == 3:
+        return "Basal cell carcinoma"
+    elif predicts == 4:
+        return "Actinic keratoses"
+    elif predicts == 5:
+        return "Vascular lesions"
+    else:
+        return "Dermatofibroma"
+
+
+@app.route('/process_image_skin', methods=['POST'])
+def process_image_skin():
+    if request.method == 'POST':
+        file = request.files['file']
+        img_bytes = file.read()  # Read image data as bytes
+        # Convert bytes to OpenCV image
+        img = cv2.imdecode(np.frombuffer(img_bytes, np.uint8), -1)
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # Convert BGR to RGB
+        result = predict2(img)  # Make prediction using img
+        disease = 'skin'
+        return docRec(disease, result, value="skin")
+
+    return 'File uploaded successfully!'
+
+
+@app.route('/skinTumor')
+def skinTumor():
+    return render_template('skinTumor.html')
+
+
+@app.route('/tumor', methods=['POST'])
+def tumor():
+    mass_npea = request.form['mass_npea']
+    size_npear = request.form['size_npear']
+    malign_ratio = request.form['malign_ratio']
+    damage_size = request.form['damage_size']
+    exposed_area = request.form['exposed_area']
+    std_dev_malign = request.form['std_dev_malign']
+    err_malign = request.form['err_malign']
+    malign_penalty = request.form['malign_penalty']
+    damage_ratio = request.form['damage_ratio']
+
+    with open('models/finalized_model_skinT_prediction_RF.sav', 'rb') as f:
+        model = pickle.load(f)
+
+    input_data = (mass_npea, size_npear, malign_ratio, damage_size, exposed_area, std_dev_malign, err_malign,
+                  malign_penalty, damage_ratio)
+    # nput_data3 = np.array(input_data).astype(int)
+
+    input_data_as_numpy_array = np.asarray(input_data)
+
+    input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)
+    result = model.predict(input_data_reshaped)
+    # convert to string with comma-separated values
+    # convert the numpy array to a string
+    result = str(result)
+
+    # remove the brackets from the string
+    result = result.replace('[', '').replace(']', '')
+    result = "Tumor size is "+result+"mm"
+    # result = ("Predicted result is"+result+"(mm)")
+    disease = 'skin'
+    return docRec(disease, result, value="skin")
+
+######################## Stroke ############################
+
+
+@app.route('/stroke')
+def stroke():
+    return render_template('strokePage.html')
+
+
+@app.route('/predictStrokepage')
+def predictStrokepage():
+    return render_template('stroke_predict.html')
+
+# predict stroke
+
+
+@app.route('/predictStroke', methods=['POST'])
+def predictStroke():
+    gender = request.form['gender']
+    age = request.form['age']
+    hypertension = request.form['hypertension']
+    heart_disease = request.form['heart_disease']
+    ever_married = request.form['ever_married']
+    work_type = request.form['work_type']
+    Residence_type = request.form['Residence_type']
+    avg_glucose_level = request.form['avg_glucose_level']
+    bmi = request.form['bmi']
+    smoking_status = request.form['smoking_status']
+
+    with open('models/finalized_model_stroke_prediction_RF.sav', 'rb') as f:
+        model = pickle.load(f)
+
+    input_data = (gender, age, hypertension, heart_disease, ever_married,
+                  work_type, Residence_type, avg_glucose_level, bmi, smoking_status)
+    # input_data3 = np.array(input_data).astype(int)
+
+    input_data_as_numpy_array = np.asarray(input_data)
+
+    input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)
+    prediction = model.predict(input_data_reshaped)
+    print(input_data)
+    if (prediction == 1):
+        result = "You are at risk of having a stroke"
+    else:
+        result = "You are not at risk of having a stroke"
+
+    return render_template('results.html', result=result)
+
+# CT image detection
+
+
+@app.route('/predictStrokeCt')
+def predictStrokeCt():
+    return render_template('stroke_detection_CT.html')
+
+
+def predict4(img):
+    model = tf.keras.models.load_model('models/finalized_model_strokeCT.h5')
+
+    # model = load_model('finalized_model_strokeCT.h5')
+
+    model = load_model('models/finalized_model_strokeCT.h5')
+
+    pic = []
+    img = cv2.resize(img, (224, 224))
+    # Convert the image to a numpy array and preprocess it
+    x = image.img_to_array(img)
+    x = np.expand_dims(x, axis=0)
+    x /= 255
+
+    # Make a prediction using the model
+    prediction = model.predict(x)
+
+    # Print the predicted class (0 = non-stroke, 1 = stroke)
+    if prediction < 0.5:
+        return ('Non-stroke')
+    else:
+        return ('Stroke')
+
+
+@app.route('/process_image_stroke', methods=['POST'])
+def process_image_stroke():
+    if request.method == 'POST':
+        file = request.files['file']
+        img_bytes = file.read()  # Read image data as bytes
+        # Convert bytes to OpenCV image
+        img = cv2.imdecode(np.frombuffer(img_bytes, np.uint8), -1)
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # Convert BGR to RGB
+        prediction = predict4(img)  # Make prediction using img
+        return render_template('results.html', result=prediction)
+
+    return 'File uploaded successfully!'
+
+
+def docRec(disease, result, value):
+    df = pd.read_excel('docs\RELEVANTDOCTORS.xlsx')
+
+    # find rows that contain a keyword in a specific column
+    keyword = str(disease)
+    column_name = 'Disease'
+    rec = df[df[column_name].str.contains(keyword)]
+
+    rec = rec.iloc[:, 1:]
+    table_classes = 'table table-striped table-bordered table-hover'
+    if value == "lung":
+        show_button = True
+        # render the output in a table format
+        return render_template('results.html', rec=rec.to_html(index=False, classes=table_classes), result=result, show_button=show_button)
+    else:
+        show_button = False
+        return render_template('results.html', rec=rec.to_html(index=False, classes=table_classes), result=result, show_button=show_button)