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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 pip install --no-cache-dir -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"]
+
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

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+import requests
+import streamlit as st
+import pandas as pd
+
+# Title of the Streamlit app
+st.title("Product Sales Prediction for Stores")
+
+# Section for single product prediction
+st.subheader("Please fill details below for sales prediction")
+
+# Input fields for user to provide single product/store details
+Product_Weight = st.number_input("Product_Weight (Weight of the product)", min_value=1, max_value=50,value=10.0)
+Product_Sugar_Content = st.selectbox("Product_Sugar_Content (Select the sugar content level of the product)", ['Low Sugar','Regular','No Sugar'])
+Product_Allocated_Area = st.number_input("Product_Allocated_Area (Area allocated for the product)", min_value=0.001,max_value=0.5,value=0.1)
+Product_Type = st.selectbox("Product_Type (Select the type of product)", ['Fruits and Vegetables','Snack Foods','Frozen Foods','Dairy','Household','Baking Goods','Canned','Health and Hygiene','Meat','Soft Drinks','Breads','Hard Drinks','Others','Starchy Foods','Breakfast','Seafood'])
+Product_MRP = st.number_input("Product_MRP (Enter the price of the product)", min_value=1,max_value=1000,value=100)
+Store_Id = st.selectbox("Store_Id (Select the Store ID)", ['OUT001','OUT002','OUT003','OUT004'])
+Store_Establishment_Year = st.selectbox("Store_Establishment_Year (Select the year the store was established)", [2009,1987,1999,1998])
+Store_Size = st.selectbox("Store_Size (Select the store size)", ["Medium", "High","Small"])
+Store_Location_City_Type = st.selectbox("Store_Location_City_Type (Select the type of city where the store is located)", ["Tier 2", "Tier 1","Tier 3"])
+Store_Type = st.selectbox("Store_Type (Select the type of store)",['Supermarket Type2','Supermarket Type1','Departmental Store','Food Mart'])
+
+# Prepare the payload to send to the backend API
+sales_data = {
+    '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_Size': Store_Size,
+    'Store_Location_City_Type': Store_Location_City_Type,
+}
+
+# When the "Predict" button is clicked, send a POST request to the backend API
+if st.button("Predict", type='primary'):
+    response = requests.post("https://parthipan00410-salespredictionbackend.hf.space/v1/salesdata", json=sales_data)
+
+    # If the request is successful, display the predicted sales
+    if response.status_code == 200:
+        result = response.json()
+        sales_prediciton = result["predicted_sales"]
+        st.write(f"Here is the predicted sales value for the product: {sales_prediciton}.")
+    else:
+        st.error("Error in API request. Please check the backend or payload format.")
+
+# Section for batch prediction
+st.subheader("Batch Prediction")
+
+# File uploader to allow CSV upload for batch prediction
+file = st.file_uploader("Upload CSV file", type=["csv"])
+
+if file is not None:
+    # When "Predict for Batch" button is clicked, send CSV to backend API
+    if st.button("Predict for Batch", type='primary'):
+        response = requests.post("https://parthipan00410-salespredictionbackend.hf.space/v1/salesdatabatch", files={"file": file})
+
+        # Display the batch prediction results if successful
+        if response.status_code == 200:
+            result = response.json()
+            st.header("Batch Prediction Results")
+            st.write(result)
+        else:
+            st.error("Error in API request. Please check the backend or CSV format.")

gradient_tuned.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:42b4d70fdf4fd9609e15aec0d5d8426f4a6b1d04b8d958f98de8c2227cfd097a
+size 1650258

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