File size: 2,812 Bytes
cc96984 |
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 |
import streamlit as st
import pandas as pd
from huggingface_hub import hf_hub_download
import joblib
import os
# -------------------------
# Configuration
# -------------------------
HF_MODEL_REPO = "VIKASHRAM/superkart"
MODEL_FILENAME = "best_model_v1.joblib"
# -------------------------
# Download & load model
# -------------------------
model = None
try:
model_path = hf_hub_download(repo_id=HF_MODEL_REPO, filename=MODEL_FILENAME, repo_type="model", token=os.getenv("HF_TOKEN"))
model = joblib.load(model_path)
st.write(f"Loaded model from Hugging Face: {HF_MODEL_REPO}/{MODEL_FILENAME}")
except Exception as e:
st.warning(f"Could not download model from Hugging Face ({HF_MODEL_REPO}).\nError: {e}\nFalling back to local file if present.")
if os.path.exists(MODEL_FILENAME):
model = joblib.load(MODEL_FILENAME)
st.write(f"Loaded local model file: {MODEL_FILENAME}")
else:
st.error("Model not available. Please upload the model to HF or place it locally.")
st.stop()
# -------------------------
# Streamlit UI
# -------------------------
st.title("SuperKart Sales Prediction App")
st.write("Predict product sales at different stores using trained ML model.")
# --- Customer details
Product_Weight = st.number_input("Product Weight", value=12.66)
Product_Sugar_Content = st.selectbox("Sugar Content", ["Low Sugar","No Sugar","Medium Sugar","High Sugar"])
Product_Allocated_Area = st.number_input("Allocated Area", value=0.027, step=0.001, format="%.3f")
Product_Type = st.text_input("Product Type", "Frozen Foods")
Product_MRP = st.number_input("Product MRP", value=117.08)
Store_Id = st.text_input("Store Id", "OUT004")
Store_Establishment_Year = st.number_input("Store Establishment Year", value=2009, step=1)
Store_Size = st.selectbox("Store Size", ["Small", "Medium", "High"])
Store_Location_City_Type = st.selectbox("Store City Type", ["Tier 1", "Tier 2", "Tier 3"])
Store_Type = st.text_input("Store Type", "Supermarket Type2")
# Assemble input into DataFrame matching training columns (raw — pipeline should handle preprocessing)
input_df = 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_Id": Store_Id,
"Store_Establishment_Year": Store_Establishment_Year,
"Store_Size": Store_Size,
"Store_Location_City_Type": Store_Location_City_Type,
"Store_Type": Store_Type
}])
st.subheader("Input Preview")
st.dataframe(input_df.T, width=700)
# Prediction
if st.button("Predict Sales"):
prediction = model.predict(input_df)
st.subheader("Prediction Result")
st.write(f"Predicted Product Store Sales Total: {prediction[0]:.2f}")
|