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()
Datasets
Models
