Upload folder using huggingface_hub

Dockerfile

@@ -1,20 +1,23 @@
-FROM python:3.13.5-slim
+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9
 
+# Set the working directory inside the container to /app
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
+# 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
 
-EXPOSE 8501
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+COPY --chown=user . $HOME/app
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# 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"]

app.py

@@ -0,0 +1,86 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+import numpy as np
+
+
+# Download and load the model
+# Ensure the HF token is available in the environment if the repo is private/gated
+model_path = hf_hub_download(repo_id= "DataWiz-6939/sales-prediction-model", filename="best_sales_prediction_model_v1.joblib")
+model = joblib.load(model_path)
+
+# Streamlit UI for Superkart Sales Prediction
+st.title("Superkart Sales Prediction App")
+st.write("""
+This application predicts the total sales for a given product in a specific store.
+Please enter the product and store details below to get a sales forecast.
+""")
+
+# User input fields based on the dataset features
+st.header("Product Details")
+product_id_prefix = st.selectbox("Product Category Prefix (from Product_Id)", ['DR', 'NC', 'FD'])
+product_id_num = st.number_input("Product ID Number (e.g., 001 for Px001)", min_value=0, max_value=999, value=np.random.randint(0,100))
+product_id_dummy = f"{product_id_prefix}{product_id_num:03d}"
+
+product_weight = st.number_input("Product Weight", min_value=1.0, max_value=50.0, value=10.0, step=0.1)
+product_sugar_content = st.selectbox("Product Sugar Content", ['low sugar', 'regular', 'no sugar'])
+product_allocated_area = st.number_input("Product Allocated Area (ratio)", min_value=0.0, max_value=0.5, value=0.05, step=0.01)
+product_type = st.selectbox("Product Type", [
+    'Dairy', 'Snack Foods', 'Household', 'Frozen Foods', 'Fruits and Vegetables',
+    'Meat', 'Breakfast', 'Seafood', 'Hard Drinks', 'Canned', 'Soft Drinks',
+    'Health and Hygiene', 'Baking Goods', 'Bread', 'Starchy Foods', 'Others'
+])
+product_mrp = st.number_input("Product MRP (Maximum Retail Price)", min_value=10.0, max_value=1000.0, value=150.0, step=1.0)
+
+st.header("Store Details")
+store_id = st.text_input("Store ID (e.g., S001)", 'S001') # Can be string as it's one-hot encoded
+store_establishment_year = st.number_input("Store Establishment Year", min_value=1900, max_value=2023, value=2000)
+store_size = st.selectbox("Store Size", ['High', 'Medium', 'Low'])
+store_location_city_type = st.selectbox("Store Location City Type", ['Tier 1', 'Tier 2', 'Tier 3'])
+store_type = st.selectbox("Store Type", ['Supermarket Type 1', 'Departmental Store', 'Supermarket Type 2', 'Food Mart'])
+
+# Assemble input into DataFrame (raw features)
+input_df = pd.DataFrame([{
+    'Product_Id': product_id_dummy, # Will be engineered
+    '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
+}])
+
+# Replicate Feature Engineering (MUST match prep.py)
+
+# 1. Extract Product Category from Product_Id
+input_df['Product_Category'] = input_df['Product_Id'].apply(lambda x: x[:2])
+product_category_map = {
+    'DR': 'Drinks',
+    'NC': 'Non-Consumable',
+    'FD': 'Food & Veg'
+}
+input_df['Product_Category'] = input_df['Product_Category'].map(product_category_map).fillna('Other')
+# Drop original Product_Id (as it's engineered)
+input_df.drop(columns=['Product_Id'], inplace=True)
+
+# 2. Compute Store Age
+current_year = 2024 # Must match year used in prep.py
+input_df['Store_Age'] = current_year - input_df['Store_Establishment_Year']
+# Drop original Store_Establishment_Year
+input_df.drop(columns=['Store_Establishment_Year'], inplace=True)
+
+# 3. Classify Food Type into Perishable category
+perishable_types = ['Meat', 'Dairy', 'Fruits and Vegetables', 'Breakfast', 'Seafood'] # Must match prep.py
+input_df['Food_Type'] = input_df['Product_Type'].apply(lambda x: 'Perishable' if x in perishable_types else 'Non-Perishable')
+
+# End Feature Engineering
+
+if st.button("Predict Sales"):
+    prediction = model.predict(input_df)[0]
+    st.subheader("Prediction Result:")
+    st.success(f"The predicted total sales for this product in the store is: **${prediction:,.2f}**")

requirements.txt

@@ -1,3 +1,8 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+huggingface_hub==0.32.6
+streamlit==1.43.2
+joblib==1.5.1
+scikit-learn==1.6.0
+xgboost==2.1.4
+mlflow==3.0.1
+