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Runtime error
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4a1859d | 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 | import streamlit as st
import pandas as pd
import requests
# Set the title of the Streamlit app
st.title("SuperKart Sales Predictor")
# Section for online prediction
st.subheader("Online Prediction")
# Collect business input for features
Product_Weight = st.number_input("Product Weight", min_value=0.0, max_value=100.0, step=0.1)
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "No Sugar", "Regular"])
Product_Type = st.selectbox("Product Type", ["Perishable", "Non Perishable"])
Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.000, max_value=0.300, step=0.1)
Product_MRP = st.number_input("Product MRP", min_value=00.00, max_value=1000.00, step=0.1)
Store_Size = st.selectbox("Store Size", ["Small", "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"])
Store_Current_Age = st.number_input("Store Current Age", min_value=0, max_value=100, step=1)
# Convert user input into a DataFrame
business_df = pd.DataFrame({
'Product_Weight': [Product_Weight],
'Product_Sugar_Content': [Product_Sugar_Content],
'Product_Type': [Product_Type],
'Product_Allocated_Area': [Product_Allocated_Area],
'Product_MRP': [Product_MRP],
'Store_Size': [Store_Size],
'Store_Location_City_Type': [Store_Location_City_Type],
'Store_Type': [Store_Type],
'Store_Current_Age': [Store_Current_Age] # Changed key name
})
# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
backend_url = "https://vrs1503-superkart-backend.hf.space/v1/predict" # Ensure correct URL
try:
response = requests.post(backend_url, json=business_df.to_dict(orient="records")[0])
response.raise_for_status() # Raise an exception for bad status codes
data = response.json()
if 'prediction' in data:
prediction = data['prediction'][0] # Access the first element of the list
st.success(f"Predicted Sales (in dollars): {prediction}")
else:
st.error(f"Error: 'prediction' key not found in response. Response: {data}")
except requests.exceptions.RequestException as e:
st.error(f"Error making prediction: {e}")
# Section for batch prediction
st.subheader("Batch Prediction")
# Allow users to upload a CSV file for batch prediction
uploaded_file = st.file_uploader("Upload a CSV file", type=["csv"])
# Make predictions when the "Predict" button is clicked
if uploaded_file is not None:
if st.button("Predict Batch"): # Changed button name to avoid duplication
backend_url = "https://vrs1503-superkart-backend.hf.space/v1/batch_predict" # Ensure correct URL
try:
response = requests.post(backend_url, files={"file": uploaded_file})
response.raise_for_status()
predictions = response.json()
st.success("Batch predictions completed!")
st.write(predictions) # Display the predictions
except requests.exceptions.RequestException as e:
st.error(f"Error making batch prediction: {e}")
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