hshivhare/AI_Doctor

1import gradio as gr2import pandas as pd3import pickle4import numpy as np5 6 7# load databasedataset===================================8df=pd.read_csv('datasets/Training.csv')9 10sym_des = pd.read_csv("datasets/symtoms_df.csv")11precautionsd = pd.read_csv("datasets/precautions_df.csv")12workoutd = pd.read_csv("datasets/workout_df.csv")13descriptiond = pd.read_csv("datasets/description.csv")14medicationsd = pd.read_csv('datasets/medications.csv')15dietsd = pd.read_csv("datasets/diets.csv")16 17 18# load model===========================================19svc = pickle.load(open('svc.pkl','rb'))20 21 22#============================================================23# custome and helping functions24#==========================helper funtions================25def helper(dis):26    desc = descriptiond[descriptiond['Disease'] == dis]['Description']27    desc = " ".join([w for w in desc])28 29    pre = precautionsd[precautionsd['Disease'] == dis][['Precaution_1', 'Precaution_2', 'Precaution_3', 'Precaution_4']]30    pre = [col for col in pre.values]31 32    med = medicationsd[medicationsd['Disease'] == dis]['Medication']33    med = [med for med in med.values]34 35    die = dietsd[dietsd['Disease'] == dis]['Diet']36    die = [die for die in die.values]37 38    wrkout = workoutd[workoutd['disease'] == dis] ['workout']39 40 41    return desc,pre,med,die,wrkout42 43symptoms_dict = {'itching': 0, 'skin_rash': 1, 'nodal_skin_eruptions': 2, 'continuous_sneezing': 3, 'shivering': 4, 'chills': 5, 'joint_pain': 6, 'stomach_pain': 7, 'acidity': 8, 'ulcers_on_tongue': 9, 'muscle_wasting': 10, 'vomiting': 11, 'burning_micturition': 12, 'spotting_ urination': 13, 'fatigue': 14, 'weight_gain': 15, 'anxiety': 16, 'cold_hands_and_feets': 17, 'mood_swings': 18, 'weight_loss': 19, 'restlessness': 20, 'lethargy': 21, 'patches_in_throat': 22, 'irregular_sugar_level': 23, 'cough': 24, 'high_fever': 25, 'sunken_eyes': 26, 'breathlessness': 27, 'sweating': 28, 'dehydration': 29, 'indigestion': 30, 'headache': 31, 'yellowish_skin': 32, 'dark_urine': 33, 'nausea': 34, 'loss_of_appetite': 35, 'pain_behind_the_eyes': 36, 'back_pain': 37, 'constipation': 38, 'abdominal_pain': 39, 'diarrhoea': 40, 'mild_fever': 41, 'yellow_urine': 42, 'yellowing_of_eyes': 43, 'acute_liver_failure': 44, 'fluid_overload': 45, 'swelling_of_stomach': 46, 'swelled_lymph_nodes': 47, 'malaise': 48, 'blurred_and_distorted_vision': 49, 'phlegm': 50, 'throat_irritation': 51, 'redness_of_eyes': 52, 'sinus_pressure': 53, 'runny_nose': 54, 'congestion': 55, 'chest_pain': 56, 'weakness_in_limbs': 57, 'fast_heart_rate': 58, 'pain_during_bowel_movements': 59, 'pain_in_anal_region': 60, 'bloody_stool': 61, 'irritation_in_anus': 62, 'neck_pain': 63, 'dizziness': 64, 'cramps': 65, 'bruising': 66, 'obesity': 67, 'swollen_legs': 68, 'swollen_blood_vessels': 69, 'puffy_face_and_eyes': 70, 'enlarged_thyroid': 71, 'brittle_nails': 72, 'swollen_extremeties': 73, 'excessive_hunger': 74, 'extra_marital_contacts': 75, 'drying_and_tingling_lips': 76, 'slurred_speech': 77, 'knee_pain': 78, 'hip_joint_pain': 79, 'muscle_weakness': 80, 'stiff_neck': 81, 'swelling_joints': 82, 'movement_stiffness': 83, 'spinning_movements': 84, 'loss_of_balance': 85, 'unsteadiness': 86, 'weakness_of_one_body_side': 87, 'loss_of_smell': 88, 'bladder_discomfort': 89, 'foul_smell_of urine': 90, 'continuous_feel_of_urine': 91, 'passage_of_gases': 92, 'internal_itching': 93, 'toxic_look_(typhos)': 94, 'depression': 95, 'irritability': 96, 'muscle_pain': 97, 'altered_sensorium': 98, 'red_spots_over_body': 99, 'belly_pain': 100, 'abnormal_menstruation': 101, 'dischromic _patches': 102, 'watering_from_eyes': 103, 'increased_appetite': 104, 'polyuria': 105, 'family_history': 106, 'mucoid_sputum': 107, 'rusty_sputum': 108, 'lack_of_concentration': 109, 'visual_disturbances': 110, 'receiving_blood_transfusion': 111, 'receiving_unsterile_injections': 112, 'coma': 113, 'stomach_bleeding': 114, 'distention_of_abdomen': 115, 'history_of_alcohol_consumption': 116, 'fluid_overload.1': 117, 'blood_in_sputum': 118, 'prominent_veins_on_calf': 119, 'palpitations': 120, 'painful_walking': 121, 'pus_filled_pimples': 122, 'blackheads': 123, 'scurring': 124, 'skin_peeling': 125, 'silver_like_dusting': 126, 'small_dents_in_nails': 127, 'inflammatory_nails': 128, 'blister': 129, 'red_sore_around_nose': 130, 'yellow_crust_ooze': 131}44diseases_list = {15: 'Fungal infection', 4: 'Allergy', 16: 'GERD', 9: 'Chronic cholestasis', 14: 'Drug Reaction', 33: 'Peptic ulcer diseae', 1: 'AIDS', 12: 'Diabetes ', 17: 'Gastroenteritis', 6: 'Bronchial Asthma', 23: 'Hypertension ', 30: 'Migraine', 7: 'Cervical spondylosis', 32: 'Paralysis (brain hemorrhage)', 28: 'Jaundice', 29: 'Malaria', 8: 'Chicken pox', 11: 'Dengue', 37: 'Typhoid', 40: 'hepatitis A', 19: 'Hepatitis B', 20: 'Hepatitis C', 21: 'Hepatitis D', 22: 'Hepatitis E', 3: 'Alcoholic hepatitis', 36: 'Tuberculosis', 10: 'Common Cold', 34: 'Pneumonia', 13: 'Dimorphic hemmorhoids(piles)', 18: 'Heart attack', 39: 'Varicose veins', 26: 'Hypothyroidism', 24: 'Hyperthyroidism', 25: 'Hypoglycemia', 31: 'Osteoarthristis', 5: 'Arthritis', 0: '(vertigo) Paroymsal  Positional Vertigo', 2: 'Acne', 38: 'Urinary tract infection', 35: 'Psoriasis', 27: 'Impetigo'}45 46# Model Prediction function47def get_predicted_value(patient_symptoms):48    input_vector = np.zeros(len(symptoms_dict))49    for item in patient_symptoms:50        input_vector[symptoms_dict[item]] = 151    return diseases_list[svc.predict([input_vector])[0]]52 53def predict_disease(symptoms):54    print(symptoms)55    disease=get_predicted_value(symptoms)56    return disease57 58def descriptionf(disease):59    desc = descriptiond[descriptiond['Disease'] == disease]['Description']60    desc = " ".join([w for w in desc])61    return desc62 63def precautionf(disease):64    pre = precautionsd[precautionsd['Disease'] == disease][['Precaution_1', 'Precaution_2', 'Precaution_3', 'Precaution_4']]65    pre = [col for col in pre.values]66    return pre[0]67 68def medicinef(disease):69    med = medicationsd[medicationsd['Disease'] == disease]['Medication']70    med = [med for med in med.values]71    return med[0]72 73def dietf(disease):74    die = dietsd[dietsd['Disease'] == disease]['Diet']75    die = [die for die in die.values]76    return die[0]77 78def workoutf(disease):79    wrkout = workoutd[workoutd['disease'] == disease] ['workout']80    return wrkout81 82def ai_doctor(symptoms):83    # print(symptoms)84    85    symptoms=list(map(str,symptoms.split()))86    predicted_disease = predict_disease(symptoms)87    print(predicted_disease)88    descriptions = descriptionf(predicted_disease)89    precaution = precautionf(predicted_disease)90    medication = medicinef(predicted_disease)91    workout = workoutf(predicted_disease)92    diet = dietf(predicted_disease)93    print(workout)94    95    return predicted_disease, descriptions, precaution, medication, workout, diet96 97# Create the Gradio interface98iface = gr.Interface(99    fn=ai_doctor, 100    inputs=['text'],  101    outputs=[102        gr.Textbox(label="Predicted Disease"), 103        gr.Textbox(label="Description"), 104        gr.Textbox(label="Precaution"), 105        gr.Textbox(label="Medication"), 106        gr.Textbox(label="Workout"), 107        gr.Textbox(label="Diet")108    ],109    title="AI Doctor",110    description="Enter your symptoms to get a predicted disease and related information."111)112 113# Launch the interface114iface.launch(share=True)115