"""

"""

from pandas.core.frame import DataFrame
import streamlit as st
import logging
from processing import infer_only_meta
import pandas as pd
import glob
import tensorflow as tf

st.set_page_config(
   page_title="Case Predictor🔎",
   page_icon="🔎",
   layout="centered",
   menu_items={
      'About': "#### Get a predicted rate of infection easily!",
      'Report a bug': "https://github.com/RutvikJ77/Case-predictor",
    }
)

models=[]
for i in glob.glob(f'./models/*'):
        models.append(tf.keras.models.load_model(i))

def analyse(user_upload):
    results, data_frame = infer_only_meta(pd.read_csv(user_upload), models, 100)
    return data_frame

def file_check(uploaded_file):
    if uploaded_file is not None:
            file_details = {"FileName":uploaded_file.name,"FileType":uploaded_file.type}

            if file_details["FileType"]!="text/csv":
                st.write("Please enter correct file type.")

def convert_df(df):
    return df.to_csv().encode('utf-8')         


st.title("Case predictor🔎")

html = """ 
<style>
    .element-container {
      padding:20px;
    }
</style>

"""

col1, col2 = st.columns((3,1))

user_upload = col1.file_uploader("Upload the observation file:", type=["csv"])
file_check(user_upload)

col2.markdown(html, unsafe_allow_html=True)

if col2.button("Analyse"):
    logging.info("Analyse called.")
    data_frame = analyse(user_upload)
    st.line_chart(data_frame)
    st.download_button(label="Download", data=convert_df(data_frame), file_name="predictions.csv", mime='text/csv')
3cb7fdb82bb6e6abc114805a2f7717314e238101
<mask token>


def convert_df(df):
    return df.to_csv().encode('utf-8')


<mask token>

<mask token>


def analyse(user_upload):
    results, data_frame = infer_only_meta(pd.read_csv(user_upload), models, 100
        )
    return data_frame


def file_check(uploaded_file):
    if uploaded_file is not None:
        file_details = {'FileName': uploaded_file.name, 'FileType':
            uploaded_file.type}
        if file_details['FileType'] != 'text/csv':
            st.write('Please enter correct file type.')


def convert_df(df):
    return df.to_csv().encode('utf-8')


<mask token>

<mask token>
st.set_page_config(page_title='Case Predictor🔎', page_icon='🔎', layout=
    'centered', menu_items={'About':
    '#### Get a predicted rate of infection easily!', 'Report a bug':
    'https://github.com/RutvikJ77/Case-predictor'})
<mask token>
for i in glob.glob(f'./models/*'):
    models.append(tf.keras.models.load_model(i))


def analyse(user_upload):
    results, data_frame = infer_only_meta(pd.read_csv(user_upload), models, 100
        )
    return data_frame


def file_check(uploaded_file):
    if uploaded_file is not None:
        file_details = {'FileName': uploaded_file.name, 'FileType':
            uploaded_file.type}
        if file_details['FileType'] != 'text/csv':
            st.write('Please enter correct file type.')


def convert_df(df):
    return df.to_csv().encode('utf-8')


st.title('Case predictor🔎')
<mask token>
file_check(user_upload)
col2.markdown(html, unsafe_allow_html=True)
if col2.button('Analyse'):
    logging.info('Analyse called.')
    data_frame = analyse(user_upload)
    st.line_chart(data_frame)
    st.download_button(label='Download', data=convert_df(data_frame),
        file_name='predictions.csv', mime='text/csv')

<mask token>
st.set_page_config(page_title='Case Predictor🔎', page_icon='🔎', layout=
    'centered', menu_items={'About':
    '#### Get a predicted rate of infection easily!', 'Report a bug':
    'https://github.com/RutvikJ77/Case-predictor'})
models = []
for i in glob.glob(f'./models/*'):
    models.append(tf.keras.models.load_model(i))


def analyse(user_upload):
    results, data_frame = infer_only_meta(pd.read_csv(user_upload), models, 100
        )
    return data_frame


def file_check(uploaded_file):
    if uploaded_file is not None:
        file_details = {'FileName': uploaded_file.name, 'FileType':
            uploaded_file.type}
        if file_details['FileType'] != 'text/csv':
            st.write('Please enter correct file type.')


def convert_df(df):
    return df.to_csv().encode('utf-8')


st.title('Case predictor🔎')
html = """ 
<style>
    .element-container {
      padding:20px;
    }
</style>

"""
col1, col2 = st.columns((3, 1))
user_upload = col1.file_uploader('Upload the observation file:', type=['csv'])
file_check(user_upload)
col2.markdown(html, unsafe_allow_html=True)
if col2.button('Analyse'):
    logging.info('Analyse called.')
    data_frame = analyse(user_upload)
    st.line_chart(data_frame)
    st.download_button(label='Download', data=convert_df(data_frame),
        file_name='predictions.csv', mime='text/csv')

"""

"""

from pandas.core.frame import DataFrame
import streamlit as st
import logging
from processing import infer_only_meta
import pandas as pd
import glob
import tensorflow as tf

st.set_page_config(
   page_title="Case Predictor🔎",
   page_icon="🔎",
   layout="centered",
   menu_items={
      'About': "#### Get a predicted rate of infection easily!",
      'Report a bug': "https://github.com/RutvikJ77/Case-predictor",
    }
)

models=[]
for i in glob.glob(f'./models/*'):
        models.append(tf.keras.models.load_model(i))

def analyse(user_upload):
    results, data_frame = infer_only_meta(pd.read_csv(user_upload), models, 100)
    return data_frame

def file_check(uploaded_file):
    if uploaded_file is not None:
            file_details = {"FileName":uploaded_file.name,"FileType":uploaded_file.type}

            if file_details["FileType"]!="text/csv":
                st.write("Please enter correct file type.")

def convert_df(df):
    return df.to_csv().encode('utf-8')         


st.title("Case predictor🔎")

html = """ 
<style>
    .element-container {
      padding:20px;
    }
</style>

"""

col1, col2 = st.columns((3,1))

user_upload = col1.file_uploader("Upload the observation file:", type=["csv"])
file_check(user_upload)

col2.markdown(html, unsafe_allow_html=True)

if col2.button("Analyse"):
    logging.info("Analyse called.")
    data_frame = analyse(user_upload)
    st.line_chart(data_frame)
    st.download_button(label="Download", data=convert_df(data_frame), file_name="predictions.csv", mime='text/csv')