Delete streamlit_app.py

streamlit_app.py

@@ -1,41 +0,0 @@
-import streamlit as st
-import joblib
-import pandas as pd
-from huggingface_hub import hf_hub_download
-
-# Load model from your HF repo
-model_file = hf_hub_download(repo_id="danialsiddiqui/task6-model", filename="model.joblib")
-model_data = joblib.load(model_file)
-model = model_data["model"]
-columns = model_data["columns"]
-
-st.title("Supermarket Revenue Prediction")
-
-# Input fields
-gender = st.selectbox("Gender", ["Male", "Female"])
-customer_type = st.selectbox("Customer Type", ["Member", "Normal"])
-product_line = st.selectbox("Product Line", ["Beverages", "Food", "Health and beauty", "Fashion", "Electronics", "Home and lifestyle", "Sports and travel"])
-unit_price = st.number_input("Unit Price", min_value=0.0, value=10.0)
-quantity = st.number_input("Quantity", min_value=1, value=1)
-tax_5 = st.number_input("Tax 5%", min_value=0.0, value=0.0)
-
-if st.button("Predict"):
-    # Create dataframe
-    df = pd.DataFrame([{
-        "gender": gender,
-        "customer_type": customer_type,
-        "product_line": product_line,
-        "unit_price": unit_price,
-        "quantity": quantity,
-        "tax_5": tax_5
-    }])
-    
-    # One-hot encode same as training
-    df = pd.get_dummies(df)
-    for col in columns:
-        if col not in df.columns:
-            df[col] = 0
-    df = df[columns]
-    
-    prediction = model.predict(df)[0]
-    st.success(f"Predicted Revenue: {prediction:.2f}")