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Dockerfile

@@ -1,12 +1,16 @@
+# 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 . .
-
-RUN pip install --no-cache-dir -r requirements.txt
 
-EXPOSE 7860
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
 
-CMD ["gunicorn", "-w", "2", "-b", "0.0.0.0:7860", "app:superkart_api"]
+# 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"]
 
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

@@ -1,41 +1,44 @@
-import numpy as np
-import joblib
-import pandas as pd
-from flask import Flask, request, jsonify
-
-# Create Flask app
-superkart_api = Flask(__name__)
-
-# Load the trained model
-model = joblib.load("tuned_gradient_boosting_regressor_model.joblib")
-
-@superkart_api.get('/')
-def home():
-    return "Welcome to the Superkart Sales Prediction API!"
-
-@superkart_api.post('/v1/predict')
-def predict_sales():
-    data = request.get_json()
-
-    sample = {
-        'Product_Weight': data['Product_Weight'],
-        'Product_Sugar_Content': data['Product_Sugar_Content'],
-        'Product_Allocated_Area': data['Product_Allocated_Area'],
-        'Product_MRP': data['Product_MRP'],
-        'Store_Size': data['Store_Size'],
-        'Store_Location_City_Type': data['Store_Location_City_Type'],
-        'Store_Type': data['Store_Type'],
-        'Product_Id_char': data['Product_Id_char'],
-        'Store_Age_Years': data['Store_Age_Years'],
-        'Product_Type_Category': data['Product_Type_Category']
-    }
-
-    input_df = pd.DataFrame([sample])
-    prediction = model.predict(input_df).tolist()[0]
-
-    return jsonify({'Sales': prediction})
-
-if __name__ == '__main__':
-    import os
-    port = int(os.environ.get("PORT", 7860))
-    superkart_api.run(host="0.0.0.0", port=port)
+import streamlit as st
+import requests
+
+API_URL = "https://Hunagypsy-superkart-backend.hf.space/v1/predict"
+
+st.title("Product Store Sales Prediction App")
+
+# User Inputs
+Product_Weight = st.number_input("Product Weight", min_value=0.0, value=12.66)
+Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+Product_Allocated_Area = st.selectbox("Product Allocated Area", ["Small", "Medium", "Large"])
+Product_MRP = st.number_input("Product MRP", min_value=0.0, value=100.0)
+Store_Size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
+Store_Type = st.selectbox("Store Type", ["Type 1", "Type 2", "Type 3", "Type 4"])
+Product_Id_char = st.text_input("Product ID (char)", value="FDX")
+Store_Age_Years = st.number_input("Store Age (Years)", min_value=0, value=5)
+Product_Type_Category = st.selectbox("Product Type Category", ["Food", "Non-Food", "Drinks"])
+
+# Package data
+product_data = {
+    "Product_Weight": Product_Weight,
+    "Product_Sugar_Content": Product_Sugar_Content,
+    "Product_Allocated_Area": Product_Allocated_Area,
+    "Product_MRP": Product_MRP,
+    "Store_Size": Store_Size,
+    "Store_Location_City_Type": Store_Location_City_Type,
+    "Store_Type": Store_Type,
+    "Product_Id_char": Product_Id_char,
+    "Store_Age_Years": Store_Age_Years,
+    "Product_Type_Category": Product_Type_Category
+}
+
+if st.button("Predict", type='primary'):
+    try:
+        response = requests.post(API_URL, json=product_data)
+        if response.status_code == 200:
+            result = response.json()
+            predicted_sales = result["Sales"]
+            st.success(f"Predicted Product Store Sales Total: ₹{predicted_sales:.2f}")
+        else:
+            st.error(f"API request failed: {response.status_code}")
+    except Exception as e:
+        st.error(f"Error: {e}")

requirements.txt

@@ -1,10 +1,2 @@
-pandas==2.2.2
-numpy==2.0.2
-scikit-learn==1.6.1
-seaborn==0.13.2
-joblib==1.4.2
-xgboost==2.1.4
-Werkzeug==2.2.2
-flask==2.2.2
-gunicorn==20.1.0
+streamlit==1.45.0
 requests==2.32.3