import pickle

from sklearn.preprocessing import LabelEncoder

import pandas as pd

import streamlit as st

import requests

from streamlit_lottie import st_lottie

from PIL import Image

st.set_page_config(page_title="Preventive Healthcare System ", page_icon="", layout="wide")

def load_lottie_url(url):

    r = requests.get(url)

    if r.status_code != 200:

        return None

    return r.json()

#Use Local CSS

def local_css(file_name):

    with open(file_name) as f:

        st.markdown(f"<style>{f.read()}</style>",unsafe_allow_html=True)

local_css("style/style.css")

def load_pickle_model(file_path):

    try:

        with open(file_path, 'rb') as file:

            model = pickle.load(file)

        print(f"Model loaded successfully from {file_path}")

        return model

    except FileNotFoundError:

        print(f"Error: The file {file_path} was not found.")

    except Exception as e:

        print(f"An error occurred while loading the model: {e}")

def get_risk_level(prob):

    if prob < 0.3:

        return "Low"

    elif 0.3 <= prob < 0.7:

        return "Moderate"

    else:

        return "High"

def generate_feedback(diabetes_prob, heart_disease_prob):

    feedback = {}

    # Risk assessment

    feedback['diabetes'] = f"Diabetes Risk: {get_risk_level(diabetes_prob)}. Probability: {diabetes_prob:.2f}"

    feedback['heart_disease'] = f"Heart Disease Risk: {get_risk_level(heart_disease_prob)}. Probability: {heart_disease_prob:.2f}"

    # Suggestions based on risk levels

    if get_risk_level(diabetes_prob) == 'High':

        feedback['diabetes_advice'] = "You are at a high risk for diabetes. Consider regular checkups, maintaining a balanced diet, and exercising regularly."

    elif get_risk_level(diabetes_prob) == 'Moderate':

        feedback['diabetes_advice'] = "You have a moderate risk of diabetes. It's recommended to monitor your sugar intake and stay active."

    if get_risk_level(heart_disease_prob) == 'High':

        feedback['heart_disease_advice'] = "You are at a high risk for heart disease. Regular cardiovascular exercise and a heart-healthy diet are recommended."

    return feedback

# --- Load assets ---

lottie_coding = load_lottie_url("https://lottie.host/d03b4e9d-a139-4df4-8f84-fb4c358891e9/gRBlHPnlpx.json")

img_contact_form = Image.open("images\\healthcare.webp")

# img_lottie_animation = Image.open("images\\pic3.jpeg")

# --- Header section ---

with st.container():

    st.title("Test How Healthy You Are ! ")

    st.title("Let's check with us ")

    st.write("")

    st.write("")

# --- What I Do ---

with st.container():

    st.write("---")

    left_column, right_column = st.columns(2)

    with left_column:

        st.header("AI Power Predictive healthcare System ")

        st.write("##")

        st.write("""

            An AI-powered solution that proactively identifies individuals at risk

of developing chronic diseases such as diabetes, heart disease, or obesity.

The system should provide personalized recommendations for preventive

care based on genetic, lifestyle, and medical data.

        """)

    with right_column:

        st_lottie(lottie_coding, height=300, key="coding")

#---services---

with st.container():

    st.write("---")

    st.header("Our Services")

    st.write("##")

    image_column, text_column = st.columns((1, 2))

    # image_column,text_column=st.columns((1,2))

    with image_column:

        st.image(img_contact_form)

    with text_column:

        st.subheader("We Specialize In Managing Chronic Diseases and Promoting Healthy Lifestyles")

        st.write(

            """

            Our healthcare services focus on helping you manage and prevent chronic diseases while promoting a healthy lifestyle. Our areas of specialization include:

            - **Diabetes Management**: Comprehensive care to monitor and control blood sugar levels.

            - **Heart Disease Prevention**: Lifestyle recommendations and medical interventions to reduce heart disease risk.

            - **Obesity Management**: Personalized plans to achieve and maintain a healthy weight.

            - **Lifestyle Modifications**: Guidance on exercise, diet, and wellness to promote overall health and well-being.

            """

        )

#     with text_column:

#         st.subheader("Integrate lottie animations inside your streamlit app")

#         st.write(

#             """

#              jhxsyfdahF

#              HDJGhfkhFyyfTF

#              IJHDGGFDyJFklgj

#              HDYUGFEUHF

#              JgdfdHKD

#              gwyuqwjkJdgd

#             """

#         )    

#         st.markdown("[Watch Video...](https://youtu.be/TXS0itGoINE)")

# ---container---

# with st.container():

#     st.write("---")

#     st.header("Get in Touch with us")

#     st.write("#")

#     contact_form="""

#     <form action="https://formsubmit.co/nimishajain2206@email.com" method="POST">

#     <input type ="hidden " name ="_captcha" value="">

#      <input type="text" name="name" palceholder ="Your Name"required>

#      <input type="email" name="email" placeholder ="Your Email" required>

#      <textarea name ="message " placeholder ="Your message here" required></textarea>

#      <button type="submit">Send</button>

#     </form>

#                  """

#     left_column,right_column=st.columns(2)

#     with left_column:

#         st.markdown(contact_form,unsafe_allow_html=True)

#     with right_column:

#         st.empty()

lbl_encoders = load_pickle_model('model\\label_encoders.pkl')

lr_dt = load_pickle_model('model\\lr_dt.pkl')

lr_ht = load_pickle_model('model\\lr_ht.pkl')

lr_ob = load_pickle_model('model\\lr_ob.pkl')

# Define categorical columns

categorical_columns = ['General_Health', 'Checkup', 'Exercise', 'Depression', 

                       'Arthritis', 'Sex', 'Age_Category', 'Smoking_History']

# Define the columns

columns = ['General_Health', 'Checkup', 'Exercise', 'Depression', 'Arthritis', 

           'Sex', 'Age_Category', 'Height_(cm)', 'Weight_(kg)', 'BMI', 

           'Smoking_History', 'Alcohol_Consumption', 'Fruit_Consumption', 

           'Green_Vegetables_Consumption', 'FriedPotato_Consumption']

# Create the form in Streamlit

st.title("Health Data Prediction Form")

with st.form(key="input_form"):

    # Collect input values from the user

    general_health = st.selectbox('General Health', ['Poor', 'Fair', 'Good', 'Very Good', 'Excellent'])

    checkup = st.selectbox('Checkup', ['Within the past year', 'Within the past 2 years', '5 or more years ago', 'Never'])

    exercise = st.selectbox('Exercise', ['Yes', 'No'])

    depression = st.selectbox('Depression', ['Yes', 'No'])

    arthritis = st.selectbox('Arthritis', ['Yes', 'No'])

    sex = st.selectbox('Sex', ['Male', 'Female'])

    age_category = st.selectbox('Age Category', ['18-24', '25-29', '30-34', '35-39', '40-44', '45-49', '50-54', 

                                                 '55-59', '60-64', '65-69', '70-74', '75-79', '80 or older'])

    height = st.number_input('Height (cm)', min_value=100.0, max_value=250.0, step=1.0)

    weight = st.number_input('Weight (kg)', min_value=20.0, max_value=200.0, step=0.1)

    bmi = st.number_input('BMI', min_value=10.0, max_value=60.0, step=0.1)

    smoking_history = st.selectbox('Smoking History', ['Yes', 'No'])

    alcohol_consumption = st.number_input('Alcohol Consumption', min_value=0.0, max_value=100.0, step=0.1)

    fruit_consumption = st.number_input('Fruit Consumption (grams/day)', min_value=0.0, max_value=500.0, step=1.0)

    green_vegetables_consumption = st.number_input('Green Vegetables Consumption (grams/day)', min_value=0.0, max_value=500.0, step=1.0)

    fried_potato_consumption = st.number_input('Fried Potato Consumption (grams/day)', min_value=0.0, max_value=500.0, step=1.0)

    # Submit button

    submit_button = st.form_submit_button(label="Submit")

if submit_button:

    # Define the input values

    input_values = {

        'General_Health': general_health,

        'Checkup': checkup,

        'Exercise': exercise,

        'Depression': depression,

        'Arthritis': arthritis,

        'Sex': sex,

        'Age_Category': age_category,

        'Height_(cm)': height,

        'Weight_(kg)': weight,

        'BMI': bmi,

        'Smoking_History': smoking_history,

        'Alcohol_Consumption': alcohol_consumption,

        'Fruit_Consumption': fruit_consumption,

        'Green_Vegetables_Consumption': green_vegetables_consumption,

        'FriedPotato_Consumption': fried_potato_consumption

    }

    # Create a DataFrame from the input values

    input_df = pd.DataFrame([input_values])

    # Encode categorical columns using the stored LabelEncoders

    for column in categorical_columns:

        if column in input_df.columns:

            input_df[column] = lbl_encoders[column].transform(input_df[column].astype(str))

    # Display the input DataFrame after encoding

    # st.write("Encoded Input DataFrame:")

    # st.write(input_df)

    # Predict using the model

    pred_dt = lr_dt.predict_proba(input_df)[0]

    pred_ht = lr_ht.predict_proba(input_df)[0]

    pred_ob = lr_ob.predict(input_df)

    print(pred_dt)

    print(pred_ht)

    print(pred_ob)

    # Display the prediction result

    st.write(f"Diabetes: {pred_dt[0]}")

    st.write(f"Hearts Decises: {pred_ht[0]}")

    st.write(f"Obesitiy: {pred_ob[0]}")

    st.title("Health Risk Assessment")

    feedback = feedback = generate_feedback(pred_dt[0], pred_ht[0])

    # Display the feedback

    for key, value in feedback.items():

        st.write(value)