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Dockerfile

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+FROM python:3.9-slim
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Copy all files from the current directory to the container's working directory
+COPY . .
+
+# Install dependencies from the requirements file without using cache to reduce image size
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Define the command to start the application using Gunicorn with 4 worker processes
+# - `-w 4`: Uses 4 worker processes for handling requests
+# - `-b 0.0.0.0:7860`: Binds the server to port 7860 on all network interfaces
+# - `app:app`: Runs the Flask app (assuming `app.py` contains the Flask instance named `app`)
+CMD streamlit run app.py --server.port 7860

app.py

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+import streamlit as st
+import pandas as pd
+import requests
+
+# Set the title of the Streamlit app
+st.title("Superkart Forecast Revenue")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect user input for property features
+product_weight = st.number_input("Product Weight", min_value=0)
+product_allocated_area = st.number_input("Product Allocated Area", min_value=0)
+product_mrp = st.number_input("Product MRP", min_value=0)
+
+product_sugar_content = st.Product_Sugar_Content("Product Sugar Content", ["Low Sugar","Regular","No Sugar","reg"])
+product_type = st.selectbox("Product Type", ["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"])
+
+store_size = st.selectbox("Store Size", ["Small","Medium","Large"])
+store_city = st.selectbox("Store Size", ["Tier 1","Tier 2","Tier 3"])
+store_type = st.selectbox("Store Type", ["Food Mart","Departmental Store","Supermarket Type1","Supermarket Type2"])
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+            'product_weight': product_weight,
+        'product_allocated_area': product_allocated_area,
+        'product_mrp': product_mrp,
+        'product_sugar_content': product_sugar_content,
+        'product_type': product_type,
+        'store_size': store_size,
+        'city_type': store_city,
+        'store_type': store_type
+}])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    response = requests.post("https://shyamgoyal-ForecastRevenueBackend.hf.space/v1/revenue", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+    if response.status_code == 200:
+        prediction = response.json()['Forecasted Revenue (in dollars)']
+        st.success(f"Forecasted Revenue Price (in dollars): {prediction}")
+    else:
+        st.error("Error making prediction.")

requirements.txt

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+pandas==2.2.2
+requests==2.28.1