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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 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

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+import streamlit as st
+import pandas as pd
+import requests
+
+# Set the title of the Streamlit app
+st.title("Superkart product sales Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction") 
+
+# Collect user input for product features
+Product_Id = st.text_input("Product ID", value="PROD001")
+Product_Weight = st.number_input("Product Weight",min_value=1, step=1, value=0)
+Product_Sugar_Content = st.selectbox("Sugar content", ['Low Sugar', 'Regular', 'No Sugar'])
+Product_Allocated_Area = st.number_input("Product_Allocated_Area", min_value=1, step=1, value=0)
+Product_Type = st.selectbox("Product Type", [
+    'Meat', 'Snack Foods', 'Hard Drinks', 'Dairy', 'Canned', 'Soft Drinks',
+    'Health and Hygiene', 'Baking Goods', 'Bread', 'Breakfast', 'Frozen Foods',
+    'Fruits and Vegetables', 'Household', 'Seafood', 'Starchy Foods', 'Others'
+] )
+Product_MRP = st.number_input("Product_MRP", min_value=1, step=0.1, value=0)
+Store_Id = st.selectbox("Store ID", ["OUT001", "OUT002", "OUT003", "OUT004"])
+Store_Establishment_Year = st.number_input("Store Established Year", min_value=1900, max_value=2050, step=1, value=2025)
+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", ["Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"])
+
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+    "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
+}])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict Sales"):
+    try:
+        response = requests.post(
+            "https://JohnsonSAimlarge-Salesforecastprediction.hf.space/v1/sales",
+            json=input_data.to_dict(orient='records')[0]
+        )
+        if response.status_code == 200:
+            predicted = response.json()['Sales']
+            st.success(f"💰 Predicted Sales: ₹{predicted}")
+        else:
+            st.error(f"❌ Backend error: {response.text}")
+    except Exception as e:
+        st.error(f"⚠️ Request failed: {e}")
+
+# Section: Batch prediction
+st.subheader("📄 Batch Prediction (CSV Upload)")
+
+uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])
+
+if uploaded_file is not None:
+    if st.button("Predict Batch Sales"):
+        try:
+            response = requests.post(
+                "https://JohnsonSAimlarge-Salesforecastprediction.hf.space/v1/salesbatch",
+                files={"file": uploaded_file}
+            )
+            if response.status_code == 200:
+                results = response.json()
+                st.success("✅ Batch predictions received!")
+                st.write(pd.DataFrame(results.items(), columns=["Product ID", "Predicted Sales (₹)"]))
+            else:
+                st.error(f"❌ Backend error: {response.text}")
+        except Exception as e:
+            st.error(f"⚠️ Request failed: {e}")
+

requirements.txt

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+pandas==2.2.2
+requests==2.28.1
+streamlit==1.43.2