Spaces:
Sleeping
Sleeping
File size: 3,310 Bytes
e9c1294 df9ec78 e9c1294 d2ee007 e9c1294 df9ec78 e9c1294 df9ec78 e9c1294 df9ec78 e9c1294 3c808fa e9c1294 f9a4ed6 3421806 e9c1294 3c808fa e9c1294 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | import streamlit as st
import pandas as pd
import requests
# Set the title of the Streamlit app
st.title("SuperKart's Deceison Making Model")
# Section for online prediction
st.subheader("Online SuperKart's Model")
# Collect user input for property features
# Product features
product_weight = st.number_input("Product Weight (in grams)", min_value=0.0, step=0.1)
product_sugar_content = st.selectbox(
"Product Sugar Content",
["Low Sugar", "Regular", "No Sugar"]
)
product_allocated_area = st.number_input(
"Producted Allocated Area (sq. ft.)", min_value=0.01, step=0.01, value=0.01
)
product_type = st.selectbox(
"Product Type",
[
"Meat",
"Snack Foods",
"Hard Drinks",
"Dairy",
"Canned",
"Soft Drinks",
"Health and Hygiene",
"Baking Goods",
"Bread",
"Breakfast",
"Frozen Foods",
"Fruits and Vegetables",
"Household",
"Seafood",
"Starchy Foods",
"Others"
]
)
product_mrp = st.number_input(
"Product MRP (in dollars)", min_value=1.0, step=0.5, value=10.0
)
store_size = st.selectbox(
"Store Size",
["Low", "Medium", "High"]
)
store_location_city_type = st.selectbox(
"Store Location City Type",
["Tier 1", "Tier 2", "Tier 3"]
)
store_type = st.selectbox(
"Store Type",
["Departmental Store", "Food Mart", "Supermarket Type1", "Supermarket Type2"]
)
# Convert user input into a DataFrame
input_data = pd.DataFrame([{
'product_weight': product_weight,
'product_sugar_content': product_sugar_content,
'product_allocated_area': product_allocated_area,
'product_type': product_type,
'product_mrp': product_mrp,
'store_size': store_size,
'store_location_city_type': store_location_city_type,
'store_type':store_type
}])
# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
print(input_data.to_dict(orient='records')[0])
# Send the input data to the Flask API for prediction
response = requests.post("https://anithajk-SuperKartDecesionMakingModelBackend.hf.space/v1/productsale", json=input_data.to_dict(orient='records')[0]) # Send data to Flask API
if response.status_code == 200:
print(f"result {response.json()}")
data = response.json()
print(data.keys())
prediction = response.json()['Total Revenue (in dollars)']
st.success(f"Total Revenue (in dollars): {prediction}")
else:
st.error("Error making prediction.")
# Section for batch prediction
st.subheader("Batch Prediction")
# Allow users to upload a CSV file for batch prediction
uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])
# Make batch prediction when the "Predict Batch" button is clicked
if uploaded_file is not None:
if st.button("Predict Batch"):
response = requests.post("https://anithajk-SuperKartDecesionMakingModelBackend.hf.space/v1/productsalebatch", files={"file": uploaded_file}) # Send file to Flask API
if response.status_code == 200:
predictions = response.json()
st.success("Batch predictions completed!")
st.write(predictions) # Display the predictions
else:
st.error("Error making batch prediction.")
|