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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 +1,124 @@
+import streamlit as st
+import requests
+import pandas as pd
+import numpy as np
+
+st.title("SuperKart Sales Prediction")
+
+st.write("Enter the product and store details to get a sales prediction.")
+
+# Input fields for product and store details
+product_id = st.text_input("Product ID")
+product_weight = st.number_input("Product Weight", min_value=0.0)
+product_sugar_content = st.selectbox("Product Sugar Content", ['Low Sugar', 'Regular', 'No Sugar', 'reg'])
+product_allocated_area = st.number_input("Product Allocated Area", min_value=0.0)
+product_type = st.selectbox("Product Type", ['Frozen Foods', 'Dairy', 'Canned', 'Baking Goods', 'Health and Hygiene', 'Snack Foods', 'Household', 'Soft Drinks', 'Breads', 'Hard Drinks', 'Others', 'Starchy Foods', 'Breakfast', 'Seafood', 'Meat', 'Fruits and Vegetables'])
+product_mrp = st.number_input("Product MRP", min_value=0.0)
+store_id = st.selectbox("Store ID", ['OUT004', 'OUT003', 'OUT001', 'OUT002']) # Based on unique values from EDA
+store_establishment_year = st.selectbox("Store Establishment Year", [1987, 1998, 1999, 2009]) # Based on unique values from EDA
+store_size = st.selectbox("Store Size", ['Medium', 'High', 'Small'])
+store_location_city_type = st.selectbox("Store Location City Type", ['Tier 2', 'Tier 1', 'Tier 3'])
+store_type = st.selectbox("Store Type", ['Supermarket Type2', 'Departmental Store', 'Supermarket Type1', 'Food Mart'])
+
+
+# Create a dictionary with the input data
+data = {
+    'Product_Id': [product_id],
+    '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]
+}
+
+# Convert the dictionary to a pandas DataFrame
+input_df = pd.DataFrame(data)
+
+# Button to trigger prediction
+if st.button("Predict Sales"):
+
+    api_url = "https://hellohatim-superkart-sales-prediction-backend.hf.space//predict"
+
+    try:
+        response = requests.post(api_url, json=input_df.to_dict(orient='records'))
+
+        if response.status_code == 200:
+            predictions = response.json()
+            st.success(f"Predicted Sales Total: {predictions[0]:.2f}")
+        else:
+            st.error(f"Error predicting sales: {response.status_code} - {response.text}")
+    except Exception as e:
+        st.error(f"An error occurred: {e}")
+
+
+if st.button("Predict"):
+    prediction = model.predict(input_df)[0]
+    probability = model.predict_proba(input_df)[0][1]
+    st.success(f"✅ Predicted Sales Total: (Confidence: {probability:.2f})")
+
+
+
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# Download and load the model
+model_path = hf_hub_download(repo_id="JohnsonSAimlarge/superkart-prediction", filename="best_superkart_model_v1.joblib")
+model = joblib.load(model_path)
+
+# ------------------------------
+# Streamlit UI
+# ------------------------------
+st.title("""Superkart""" + " - Sales Prediction ""superkart sales Prediction App")
+
+st.write("""
+This application predicts potential sales for superkart.
+""")
+
+
+# Input fields for product and store details
+product_id = st.text_input("Product ID")
+product_weight = st.number_input("Product Weight", min_value=0.0)
+product_sugar_content = st.selectbox("Product Sugar Content", ['Low Sugar', 'Regular', 'No Sugar', 'reg'])
+product_allocated_area = st.number_input("Product Allocated Area", min_value=0.0)
+product_type = st.selectbox("Product Type", ['Frozen Foods', 'Dairy', 'Canned', 'Baking Goods', 'Health and Hygiene', 'Snack Foods', 'Household', 'Soft Drinks', 'Breads', 'Hard Drinks', 'Others', 'Starchy Foods', 'Breakfast', 'Seafood', 'Meat', 'Fruits and Vegetables'])
+product_mrp = st.number_input("Product MRP", min_value=0.0)
+store_id = st.selectbox("Store ID", ['OUT004', 'OUT003', 'OUT001', 'OUT002']) # Based on unique values from EDA
+store_establishment_year = st.selectbox("Store Establishment Year", [1987, 1998, 1999, 2009]) # Based on unique values from EDA
+store_size = st.selectbox("Store Size", ['Medium', 'High', 'Small'])
+store_location_city_type = st.selectbox("Store Location City Type", ['Tier 2', 'Tier 1', 'Tier 3'])
+store_type = st.selectbox("Store Type", ['Supermarket Type2', 'Departmental Store', 'Supermarket Type1', 'Food Mart'])
+
+
+# Create a dictionary with the input data
+data = {
+    'Product_Id': [product_id],
+    '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]
+}
+
+
+input_df = pd.DataFrame([input_data])
+
+# ------------------------------
+# Prediction
+# ------------------------------
+
+if st.button("Predict"):
+    prediction = model.predict(input_df)[0]
+    probability = model.predict_proba(input_df)[0][1]
+    st.success(f"✅ Predicted Sales Total: (Confidence: {probability:.2f})")

requirements.txt

@@ -1,3 +1,7 @@
-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