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Dockerfile

@@ -1,20 +1,16 @@
-FROM python:3.13.5-slim
+# 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
 
-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
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+# 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"]
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

@@ -0,0 +1,51 @@
+import requests
+import streamlit as st
+import pandas as pd
+
+st.title("🛒 Sales Forecasting App")
+st.subheader("🔮 Online Sales Prediction")
+
+# Input fields for product & store data
+Product_Sugar_Content = st.selectbox("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", "Bread", "Breads", "Hard Drinks",
+    "Others", "Starchy Foods", "Breakfast", "Seafood"
+])
+Store_Id = st.selectbox("Store Id", ["OUT001", "OUT002", "OUT003", "OUT004"])
+Store_Size = st.selectbox("Store Size", ["Medium", "High", "Low", "Small"])
+Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
+Store_Type = st.selectbox("Store Type", ["Departmental Store", "Supermarket Type1", "Supermarket Type2", "Food Mart"])
+
+Product_Weight = st.number_input("Product Weight (kg)", min_value=0.0, value=5.0)
+Product_Price = st.number_input("Product Price ($)", min_value=0.0, value=50.0)
+Store_Area = st.number_input("Store Area (sq.ft)", min_value=0.0, value=2000.0)
+
+# Prepare input for API
+sales_data = {
+    "Product_Sugar_Content": Product_Sugar_Content,
+    "Product_Type": Product_Type,
+    "Store_Id": Store_Id,
+    "Store_Size": Store_Size,
+    "Store_Location_City_Type": Store_Location_City_Type,
+    "Store_Type": Store_Type,
+    "Product_Weight": Product_Weight,
+    "Product_Price": Product_Price,
+    "Store_Area": Store_Area
+}
+
+if st.button("Predict Sales", type='primary'):
+    try:
+        response = requests.post(
+            "https://ankushwaghmare-backend.hf.space/v1/sales_forecast", 
+            json=sales_data
+        )
+        if response.status_code == 200:
+            result = response.json()
+            predictionResult = result["Prediction"]
+            st.write(f"ased on the information provided, the prediction is likely to {predictionResult}.")
+        else:
+            st.error(f"API Error {response.status_code}: {response.text}")
+    except Exception as e:
+        st.error(f"Request failed: {e}")

requirements.txt

@@ -1,3 +1,3 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+requests==2.28.1
+streamlit==1.43.2