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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 7860 and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--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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+from datetime import datetime
+import streamlit as st
+import pandas as pd
+import requests
+
+# Set the title of the Streamlit app
+st.title("SuperKart Sales Forecast System")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect user input for Product and Store features
+Product_Id = st.text_input("Product_Id")
+Product_Weight = st.number_input("Product_Weight", min_value=1.0,max_value=100.0, value=1.0)
+Product_Sugar_Content = st.selectbox("Product_Sugar_Content", ["No Sugar", "Low Sugar", "Regular"])
+Product_Allocated_Area = st.number_input("Product_Allocated_Area", min_value=0.0,max_value=1.0,value=.5)
+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"])
+Product_MRP = st.number_input("Product_MRP", min_value=0.0)
+#Store_Id = st.selectbox("Store_Id", ["OUT001", "OUT002", "OUT003","OUT004"])
+Store_Establishment_Year = st.number_input("Store_Establishment_Year", min_value=1987,max_value=2025,value=2009)
+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
+}])
+
+# Extract the Product_Code and Store_Age before feeding to the model
+input_data["Product_Code"] = input_data["Product_Id"].str[:2]
+input_data.drop("Product_Id", axis=1, inplace=True)
+    
+current_year = datetime.now().year
+input_data["Store_Age"] = current_year - input_data["Store_Establishment_Year"]
+input_data.drop("Store_Establishment_Year", axis=1, inplace=True)
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Forecast"):
+    try:
+        response = requests.post(
+            "https://UncloudMe-SK_Sales_Forecast.hf.space/salespredict",
+            json=input_data.to_dict(orient='records')[0],
+            timeout=30  # add timeout for safety
+        )
+
+        if response.status_code == 200:
+            prediction = response.json().get('Predicted Sales', None)
+            if prediction is not None:
+                st.success(f"Predicted Sales: {prediction}")
+            else:
+                st.error("No prediction found in response.")
+        else:
+            # show backend error text if available
+            st.error(f"Error {response.status_code}: {response.text}")
+
+    except requests.exceptions.RequestException as e:
+        # catch all connection, timeout, DNS, etc. errors
+        st.error(f"Connection error: {str(e)}")
+
+
+#if st.button("Forecast"):
+#    response = requests.post("https://UncloudMe-SK_Sales_Forecast.hf.space/salespredict", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+#    if response.status_code == 200:
+#        prediction = response.json()['Predicted Sales']
+#        st.success(f"Predicted Sales: {prediction}")
+#    else:
+#        st.error("Error making prediction.")
+
+# Section for batch prediction
+st.subheader("Batch Prediction")
+# Allow users to upload a CSV file for batch prediction
+uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])
+
+# Make batch prediction when the "Predict Batch" button is clicked
+if uploaded_file is not None:
+    if st.button("Predict Batch"):
+        response = requests.post("https://UncloudMe-SK_Sales_Forecast.hf.space/salespredictbatch", files={"file": uploaded_file})  # Send file to Flask API
+        if response.status_code == 200:
+            predictions = response.json()
+            st.success("Batch predictions completed!")
+            st.write(predictions)  # Display the predictions
+        else:
+            st.error("Error making batch prediction.")

requirements.txt

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