app.py

import gradio as gr
from huggingface_hub import InferenceClient
import numpy as np
import pandas as pd
import joblib


import pickle

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
cols = pd.read_csv('./Columns.csv')
cols = cols['Cols']
#with open("./model.pkl", "rb") as f:
 #   model = pickle.load(f)
model = joblib.load("./model.pkl")

severityDictionary=dict()
description_list = dict()
precautionDictionary=dict()
symptoms_dict = {}
x = cols
for index, symptom in enumerate(x):
       symptoms_dict[symptom] = index

def calc_condition(exp,days):
    sum=0
    for item in exp:
         sum=sum+severityDictionary[item]
    if((sum*days)/(len(exp)+1)>13):
        print("You should take the consultation from doctor. ")
    else:
        print("It might not be that bad but you should take precautions.")

def getDescription():
    global description_list
    with open('/kaggle/input/dataset/symptom_Description.csv') as csv_file:
        csv_reader = csv.reader(csv_file, delimiter=',')
        line_count = 0
        for row in csv_reader:
            _description={row[0]:row[1]}
            description_list.update(_description)

def getSeverityDict():
    global severityDictionary
    with open('/kaggle/input/dataset/Symptom_severity.csv') as csv_file:
        csv_reader = csv.reader(csv_file, delimiter=',')
        line_count = 0
        try:
            for row in csv_reader:
                _diction={row[0]:int(row[1])}
                severityDictionary.update(_diction)
        except:
            pass

def getprecautionDict():
    global precautionDictionary
    with open('/kaggle/input/dataset/symptom_precaution.csv') as csv_file:
        csv_reader = csv.reader(csv_file, delimiter=',')
        line_count = 0
        for row in csv_reader:
            _prec={row[0]:[row[1],row[2],row[3],row[4]]}

            precautionDictionary.update(_prec)

def getInfo():
    print("-----------------------------------HealthCare ChatBot-----------------------------------")
    print("\nYour Name? \t\t\t\t",end="->")
    name=input("")
    print("Hello", name)

def check_pattern(dis_list,inp):
    pred_list=[]
    inp=inp.replace(' ','_')
    patt = f"{inp}"
    regexp = re.compile(patt)
    pred_list=[item for item in dis_list if regexp.search(item)]
    if(len(pred_list)>0):
        return 1,pred_list
    else:
        return 0,[]

def sec_predict(symptoms_exp):
    df = pd.read_csv('/kaggle/input/dataset/Training.csv')
    X = df.iloc[:, :-1]
    y = df['prognosis']
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=20)
    rf_clf = DecisionTreeClassifier()
    rf_clf.fit(X_train, y_train)
    symptoms_dict = {symptom: index for index, symptom in enumerate(X)}
    input_vector = np.zeros(len(symptoms_dict))

    for item in symptoms_exp:

      input_vector[[symptoms_dict[item]]] = 1



    return rf_clf.predict([input_vector])

def print_disease(node):
    node = node[0]
    val  = node.nonzero()
    disease = le.inverse_transform(val[0])
    return list(map(lambda x:x.strip(),list(disease)))


def tree_to_code(tree, feature_names):
    getSeverityDict()
    getDescription()
    getprecautionDict()
    getInfo()
    tree_ = tree.tree_
    feature_name = [
        feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!"
        for i in tree_.feature
    ]
    chk_dis=",".join(feature_names).split(",")
    symptoms_present = []
    while True:
       # engine.say("\n Enter the symptom you are experiencing \t\t\t",)
        #engine.runAndWait()
        print("\nEnter the symptom you are experiencing  \t\t",end="->")
        disease_input = input("")
        conf,cnf_dis=check_pattern(chk_dis,disease_input)
        if conf==1:
            print("searches related to input: ")
            for num,it in enumerate(cnf_dis):
                print(num,")",it)
            if num!=0:
                print(f"Select the one you meant (0 - {num}):  ", end="")
                conf_inp = int(input(""))
            else:
                conf_inp=0
            disease_input=cnf_dis[conf_inp]
            break
        else:
            print("Enter valid symptom.")
    while True:
        try:
            num_days=int(input("Okay. From how many days ? : "))
            break
        except:
            print("Enter valid input.")
            
    def recurse(node, depth):
        indent = "  " * depth
        if tree_.feature[node] != _tree.TREE_UNDEFINED:
            name = feature_name[node]
            threshold = tree_.threshold[node]
            if name == disease_input:
                val = 1
            else:
                val = 0
            if  val <= threshold:
                recurse(tree_.children_left[node], depth + 1)
            else:
                symptoms_present.append(name)
                recurse(tree_.children_right[node], depth + 1)
        else:
            present_disease = print_disease(tree_.value[node])
            red_cols = reduced_data.columns
            symptoms_given = red_cols[reduced_data.loc[present_disease].values[0].nonzero()]
            #engine.say("Are you experiencing any")
            #engine.runAndWait()
            print("Are you experiencing any ")
            symptoms_exp=[]
            for syms in list(symptoms_given):
                inp=""
               # engine.say(f"{syms}, are you experiencing it?")
                #engine.runAndWait()
                print(syms,"? : ",end='')
                while True:
                    inp=input("")
                    if(inp=="yes" or inp=="no"):
                        break
                    else:
                        print("provide proper answers i.e. (yes/no) : ",end="")
                if(inp=="yes"):
                    symptoms_exp.append(syms)

            second_prediction=sec_predict(symptoms_exp)
            calc_condition(symptoms_exp,num_days)
            if(present_disease[0]==second_prediction[0]):
               # engine.say("You may have ", present_disease[0])
                #engine.runAndWait()
                print("You may have ", present_disease[0])
                print(description_list[present_disease[0]])
            else:
                print("You may have ", present_disease[0], "or ", second_prediction[0])
                print(description_list[present_disease[0]])
                print(description_list[second_prediction[0]])

            precution_list=precautionDictionary[present_disease[0]]
            print("Take following measures : ")
            for  i,j in enumerate(precution_list):
                print(i+1,")",j)


    recurse(0, 1)
    print("----------------------------------------------------------------------------------------------------------------------------------")








            
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    tree_to_code(model,cols),
)


if __name__ == "__main__":
    demo.launch()