Upload Streamlit app and model

Dockerfile

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+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9-slim
+
+# Set the working directory inside the container to /app
+WORKDIR /app
+
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
+
+# Install Python dependencies listed in requirements.txt
+RUN pip3 install -r requirements.txt
+
+# Define the command to run the Streamlit app on port 8501 and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

SuperKart_Mode_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ff5602dc4d1b205e75d1f0a9fe99155e63c1e1264c99358453d5132066bfb1df
+size 621942

app.py

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+
+import streamlit as st
+import pandas as pd
+import joblib
+
+# Load trained model pipeline
+model = joblib.load('deployment_files/SuperKart_Mode_v1_0.joblib')
+
+# Streamlit app title
+st.title("🛒 Product Store Sales Prediction App")
+st.markdown("Enter product and store details below to predict sales.")
+
+# Input form
+with st.form("prediction_form"):
+    product_weight = st.number_input("Product Weight (in kg)", min_value=1.0, max_value=50.0, value=13.5)
+    product_allocated_area = st.number_input("Allocated Area (0 to 1)", min_value=0.001, max_value=1.0, value=0.08)
+    product_mrp = st.number_input("Product MRP (₹)", min_value=1.0, max_value=1000.0, value=250.75)
+    store_age = st.number_input("Store Age (in years)", min_value=1, max_value=100, value=20)
+
+    product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar", "High Sugar"])
+    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"
+    ])
+    store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+    store_location = st.selectbox("City Tier", ["Tier 1", "Tier 2", "Tier 3"])
+    store_type = st.selectbox("Store Type", ["Departmental Store", "Supermarket Type1", "Supermarket Type2", "Food Mart"])
+
+    submit = st.form_submit_button("Predict Sales")
+
+# Prediction logic
+if submit:
+    input_dict = {
+        "Product_Weight": [product_weight],
+        "Product_Allocated_Area": [product_allocated_area],
+        "Product_MRP": [product_mrp],
+        "Store_Age": [store_age],
+        "Product_Sugar_Content": [product_sugar_content],
+        "Product_Type": [product_type],
+        "Store_Size": [store_size],
+        "Store_Location_City_Type": [store_location],
+        "Store_Type": [store_type]
+    }
+
+    input_df = pd.DataFrame(input_dict)
+
+    # Predict using loaded model
+    prediction = model.predict(input_df)[0]
+
+    # Show result
+    st.success(f"📈 Predicted Product Store Sales: ₹{prediction:,.2f}")

requirements.txt

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+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+streamlit==1.43.2