import gradio as gr
import pickle
from sklearn import preprocessing
import pandas as pd

filename = 'knn_model.sav'

loaded_model = pickle.load(open(filename, 'rb'))



def hptension(hp):
  if hp == 'yes':
      return 1
  else:
      return 0

def ht_dis(ht):
  if ht == 'yes':
      return 1
  else:
      return 0
    

def gender_select(gen):
  if gen == 'male':
      return 1
  else:
      return 0
    
def age_group_selector(age_grp):
  if age_grp == '0-16': return 0
  elif age_grp =='17-32': return 1
  elif age_grp =='33-48': return 2
  elif age_grp =='49-64': return 3
  else: return 4
  

def smoker_cat(smoke):
  if smoke == 'formerly smoked': return 0
  elif smoke =='never smoked': return 1
  elif smoke =='smokes': return 2
  else: return 3


def predict_insurance(input_gender,input_age_group,input_hypertension,input_heart_disease,input_avg_glucose_level,input_bmi,input_smoking_status):
    
    input_gender,input_age_group,input_hypertension,input_heart_disease,input_avg_glucose_level,input_bmi,input_smoking_status = input_gender,input_age_group,input_hypertension,input_heart_disease,input_avg_glucose_level,input_bmi,input_smoking_status

    series = {'gender': [gender_select(input_gender)],
            'age_band': [age_group_selector(input_age_group)],
          'hypertension': [hptension(input_hypertension)],
          'heart_disease': [ht_dis(input_heart_disease)],
          'avg_glucose_level': [input_avg_glucose_level /272],
          'bmi': [input_bmi/49],
          'smoking_status': [smoker_cat(input_smoking_status)],
           }

    vector = pd.DataFrame(series)

    result = loaded_model.predict(vector)
    if result[0] == 1:
      return "Risk of having stroke is high"
    else:
      return "Risk of having stroke is low"

css = """
footer {display:none !important}
.output-markdown{display:none !important}
footer {visibility: hidden} 

.gr-button-lg {
    z-index: 14;
    width: 113px;
    height: 30px;
    left: 0px;
    top: 0px;
    padding: 0px;
    cursor: pointer !important; 
    background: none rgb(17, 20, 45) !important;
    border: none !important;
    text-align: center !important;
    font-size: 14px !important;
    font-weight: 500 !important;
    color: rgb(255, 255, 255) !important;
    line-height: 1 !important;
    border-radius: 6px !important;
    transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
    box-shadow: none !important;
}
.gr-button-lg:hover{
    z-index: 14;
    width: 113px;
    height: 30px;
    left: 0px;
    top: 0px;
    padding: 0px;
    cursor: pointer !important; 
    background: none rgb(66, 133, 244) !important;
    border: none !important;
    text-align: center !important;
    font-size: 14px !important;
    font-weight: 500 !important;
    color: rgb(255, 255, 255) !important;
    line-height: 1 !important;
    border-radius: 6px !important;
    transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
    box-shadow: rgb(0 0 0 / 23%) 0px 1px 7px 0px !important;
}

"""

with gr.Blocks(title="Brain Stroke Prediction | Data Science Dojo", css = css) as demo:
    with gr.Row():
        input_gender = gr.Radio(["male", "female"],label="Gender")
        input_hypertension = gr.Radio(["yes", "no"],label="Hypertension")
        input_heart_disease = gr.Radio(["yes", "no"],label="Heart disease")
    with gr.Row():
        input_age_group = gr.Dropdown(['0-16','17-32','33-48','49-64','64+'],label='Age Group')
        input_smoking_status = gr.Dropdown(['formerly smoked', 'never smoked', 'smokes', 'Prefer not to say'],label='Smoker')
    with gr.Row():
        input_avg_glucose_level =  gr.Slider(0, 270,label='Average Glucose Level')
    with gr.Row():
        input_bmi =  gr.Slider(0, 45,label='BMI Range')
    with gr.Row():
        stroke = gr.Textbox(label='Chances of stroke')
    btn_ins = gr.Button(value="Submit")
    btn_ins.click(fn=predict_insurance, inputs=[input_gender,input_age_group,input_hypertension,input_heart_disease,
                                                input_avg_glucose_level,input_bmi,input_smoking_status], outputs=[stroke])

demo.launch()