import streamlit as st

import requests

import pandas as pd

# Define the FastAPI endpoint

url = "http://20.253.116.228/predict"

# Style for the result

st.markdown("""

    <style>

    .big-font {

        font-size:30px !important;

        font-weight: bold;

    }

    </style>

    """, unsafe_allow_html=True)

# Function to translate prediction to human-readable form

def interpret_prediction(prediction):

    if prediction == 0:

        return "Insuffient Weight"

    elif prediction == 1:

        return "Healthy Weight"

    elif prediction == 2:

        return "Overweight Level 1"

    elif prediction == 3:

        return "Overweight Level 2"

    elif prediction == 4:

        return "Obesity Level 1"

    elif prediction == 5:

        return "Obesity Level 2"

    elif prediction == 6:

        return "Obesity Level 3"

    else:

        return "Unknown Level"

def main():

    st.title("Obesity Level Prediction")

    # User can choose to input data via form or upload CSV

    input_method = st.radio("How would you like to input data?", ('Fill Form', 'Upload CSV'))

    if input_method == 'Fill Form':

         # Initialize form fields with default values

        form_data = {

            "id": 0,

            "Gender": 'Male',

            "Age": 25.0,

            "Height": 1.75,

            "Weight": 70.0,

            "family_history_with_overweight": 'yes',

            "FAVC": 'yes',

            "FCVC": 2.0,

            "NCP": 3.0,

            "CAEC": 'Sometimes',

            "SMOKE": 'no',

            "CH2O": 2.0,

            "SCC": 'no',

            "FAF": 1.0,

            "TUE": 2.0,

            "CALC": 'Sometimes',

            "MTRANS": 'Public_Transportation'

        }

        # Create form to input data or display data from CSV

        with st.form(key='my_form'):

            id_field = st.number_input('ID', value=form_data["id"])

            gender = st.selectbox('Gender', ['Male', 'Female'], index=['Male', 'Female'].index(form_data["Gender"]))

            age = st.number_input('Age', min_value=18.00, max_value=100.00, value=form_data["Age"])

            height = st.number_input('Height (meters)', value=form_data["Height"])

            weight = st.number_input('Weight (kg)', value=form_data["Weight"])

            family_history_with_overweight = st.selectbox('Family history of overweight', ['yes', 'no'], index=['yes', 'no'].index(form_data["family_history_with_overweight"]))

            favc = st.selectbox('Frequent consumption of high caloric food', ['yes', 'no'], index=['yes', 'no'].index(form_data["FAVC"]))

            fcvc = st.number_input('Frequency of vegetables consumption', min_value=1.00, max_value=3.00, value=form_data["FCVC"])

            ncp = st.number_input('Number of main meals', value=form_data["NCP"])

            caec = st.selectbox('Consumption of food between meals', ['no', 'Sometimes', 'Frequently', 'Always'], index=['no', 'Sometimes', 'Frequently', 'Always'].index(form_data["CAEC"]))

            smoke = st.selectbox('Smoking', ['yes', 'no'], index=['yes', 'no'].index(form_data["SMOKE"]))

            ch2o = st.number_input('Consumption of water daily (liters)', value=form_data["CH2O"])

            scc = st.selectbox('Calories consumption monitoring', ['yes', 'no'], index=['yes', 'no'].index(form_data["SCC"]))

            faf = st.number_input('Physical activity frequency (times per week)', value=form_data["FAF"])

            tue = st.number_input('Time using electronic devices (hours per day)', value=form_data["TUE"])

            calc = st.selectbox('Consumption of alcohol', ['no', 'Sometimes', 'Frequently'], index=['no', 'Sometimes', 'Frequently'].index(form_data["CALC"]))

            mtrans = st.selectbox('Transportation used', ['Automobile', 'Motorbike', 'Bike', 'Public_Transportation', 'Walking'], index=['Automobile', 'Motorbike', 'Bike', 'Public_Transportation', 'Walking'].index(form_data["MTRANS"]))

            submit_button = st.form_submit_button(label='Predict Obesity Level')

            # Handle form submission

        if submit_button:

            # Construct the request payload

            data = {

                "id": id_field,

                "Gender": gender,

                "Age": age,

                "Height": height,

                "Weight": weight,

                "family_history_with_overweight": family_history_with_overweight,

                "FAVC": favc,

                "FCVC": fcvc,

                "NCP": ncp,

                "CAEC": caec,

                "SMOKE": smoke,

                "CH2O": ch2o,

                "SCC": scc,

                "FAF": faf,

                "TUE": tue,

                "CALC": calc,

                "MTRANS": mtrans

            }

        # Send a post request to the server

        response = requests.post(url, json=data)

        if response.status_code == 200:

            result = response.json()

            # Interpret the prediction for the user

            prediction = interpret_prediction(result.get('prediction', -1))

            # Display the prediction result

            st.markdown(f'<p class="big-font">Obesity Level: {prediction}</p>', unsafe_allow_html=True)

        else:

            st.error("Failed to get a valid response from the model.")

    elif input_method == 'Upload CSV':

        uploaded_file = st.file_uploader("Choose a CSV file", type="csv")

        if uploaded_file is not None:

            dataframe = pd.read_csv(uploaded_file)

            # Create a new column for predictions in the dataframe

            dataframe['Obesity Level'] = None

            # Iterate over the rows of the dataframe and make predictions

            with st.spinner('Making predictions...'):

                for index, row in dataframe.iterrows():

                    # Convert the row to dictionary

                    data = row.to_dict()

                    print(data)

                    # Remove the 'Obesity Level' key if present

                    data.pop('Obesity Level', None)

                    # Send a post request to the server

                    response = requests.post(url, json=data)

                    if response.status_code == 200:

                        result = response.json()

                        # Interpret the prediction for the user

                        prediction = interpret_prediction(result.get('prediction', -1))

                        # Update the dataframe with predictions

                        dataframe.at[index, 'Obesity Level'] = prediction

                    else:

                        st.error(f"Failed to get a valid response from the model for row {index+1}: {response.text}")

                        break  # Stop the loop if there is an error

            # Only proceed if all predictions were successful

            if not dataframe['Obesity Level'].isnull().any():

                st.success('All predictions made successfully!')

                # Display the dataframe with predictions

                st.dataframe(dataframe)

                # Allow the user to download the augmented CSV

                st.download_button(

                    label="Download CSV with predictions",

                    data=dataframe.to_csv(index=False),

                    file_name='predictions.csv',

                    mime='text/csv'

                )

            else:

                st.error(f"Failed to get a valid response from the model: {response.text}")

if __name__ == "__main__":

    main()