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DockerFile

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+# Base image with Python
+FROM python:3.10-slim
+
+# Set environment variables
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+ENV PORT=7860
+
+# Set working directory
+WORKDIR /app
+
+# Copy files
+COPY requirements.txt .
+COPY app.py .
+COPY super_kart_prediction_model_v1_0.joblib .
+
+# Install dependencies
+RUN pip install --upgrade pip && pip install -r requirements.txt
+
+# Expose the Streamlit port
+EXPOSE 7860
+
+# Run the Streamlit app on port 7860 (as required by Hugging Face)
+CMD streamlit run app.py --server.port 7860 --server.address 0.0.0.0

DockerFileUI

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+# Base image with Python
+FROM python:3.10-slim
+
+# Set environment variables
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+ENV PORT=7860
+
+# Set working directory
+WORKDIR /app
+
+# Copy files
+COPY requirementsUI.txt .
+COPY streamlit_app.py .
+COPY super_kart_prediction_model_v1_0.joblib .
+
+# Install dependencies
+RUN pip install --upgrade pip && pip install -r requirementsUI.txt
+
+# Expose the Streamlit port
+EXPOSE 7860
+
+# Run the Streamlit app on port 7860 (as required by Hugging Face)
+CMD streamlit run streamlit_app.py --server.port 7860 --server.address 0.0.0.0

app.py

@@ -1,20 +1,10 @@
+# Define the backend
 from flask import Flask, request, jsonify
 import joblib
 import pandas as pd
-import os
-import numpy
-import sklearn.compose._column_transformer
-class _RemainderColsList(list): pass
-sklearn.compose._column_transformer._RemainderColsList = _RemainderColsList
-
-
-# Optional: ignore ComplexWarnings if they’re not needed
-import warnings
-warnings.filterwarnings("ignore", category=UserWarning)  # Or remove entirely
 
 # Load the trained model pipeline
-model_path = os.path.join("super_kart_prediction_model_v1_0.joblib")
-model = joblib.load(model_path)
+model = joblib.load("deployment_files/super_kart_prediction_model_v1_0.joblib")
 
 # Initialize the Flask app
 app = Flask(__name__)

requirements.txt

@@ -1,6 +1,6 @@
 flask
-scikit-learn==1.3.2
+scikit-learn
 pandas
-numpy==1.26.4
+numpy
 xgboost
 joblib

requirementsUI.txt

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+streamlit
+pandas
+scikit-learn
+joblib

streamlit_app.py

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+import streamlit as st
+import pandas as pd
+import joblib
+
+# Load the trained model
+model = joblib.load("super_kart_prediction_model_v1_0.joblib")
+
+st.set_page_config(page_title="SuperKart Sales Predictor", layout="centered")
+
+st.title("SuperKart Product Sales Prediction")
+
+# --- Input form ---
+with st.form("prediction_form"):
+    col1, col2 = st.columns(2)
+
+    with col1:
+        product_weight = st.slider("Product Weight (kg)", 0.0, 25.0, 10.0)
+        allocated_area = st.slider("Allocated Area (sq ft)", 50.0, 1000.0, 200.0)
+        product_mrp = st.number_input("Product MRP (₹)", min_value=10.0, max_value=500.0, value=250.0)
+
+    with col2:
+        sugar_content = st.selectbox("Sugar Content", ["Low", "Medium", "High"])
+        product_type = st.selectbox("Product Type", ["Snack Foods", "Baking Goods", "Canned", "Soft Drinks", "Dairy"])
+        store_year = st.selectbox("Store Establishment Year", list(range(1985, 2024)))
+
+        store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+        city_type = st.selectbox("Store Location City Type", ["Urban", "Semi-Urban", "Rural"])
+        store_type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Grocery Store", "Supermarket Type3"])
+
+    submitted = st.form_submit_button("Predict")
+
+# --- Predict ---
+if submitted:
+    # Create input DataFrame
+    input_data = pd.DataFrame([{
+        "Product_Weight": product_weight,
+        "Product_Sugar_Content": sugar_content,
+        "Product_Allocated_Area": allocated_area,
+        "Product_Type": product_type,
+        "Product_MRP": product_mrp,
+        "Store_Establishment_Year": store_year,
+        "Store_Size": store_size,
+        "Store_Location_City_Type": city_type,
+        "Store_Type": store_type
+    }])
+
+    # Make prediction
+    prediction = model.predict(input_data)[0]
+    st.success(f"📈 Predicted Product Sales: ₹{round(prediction, 2)}")