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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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+
+# Set the title of the Streamlit app
+st.title("SuperKart Product Sales Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+st.header("Enter Product and Store Details")
+
+# Collect user input for product store features
+
+product_weight = st.number_input(
+    "Product Weight (in kg)", min_value=0.0, step=0.1, value=10.0
+)
+
+product_sugar_content = st.selectbox(
+    "Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"]
+)
+
+product_allocated_area = st.number_input(
+    "Product Allocated Area (store fraction)", min_value=0.0, max_value=1.0, step=0.01, value=0.05
+)
+
+product_type = st.selectbox(
+    "Product Type",
+    [
+        "Frozen Foods", "Dairy", "Canned", "Baking Goods", "Health and Hygiene",
+        "Snack Foods", "Soft Drinks", "Meat", "Fruits and Vegetables", "Breads",
+        "Breakfast Foods", "Starchy Foods", "Seafood", "Household", "Others"
+    ]
+)
+
+product_mrp = st.number_input(
+    "Product MRP (Maximum Retail Price)", min_value=0.0, step=1.0, value=150.0
+)
+
+store_establishment_year = st.number_input(
+    "Store Establishment Year", min_value=1900, max_value=2025, step=1, value=2005
+)
+
+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_Weight': product_weight,
+    'Product_Sugar_Content': product_sugar_content,
+    'Product_Allocated_Area': product_allocated_area,
+    'Product_Type': product_type,
+    'Product_Mrp': product_mrp,
+    '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"):
+    #response = requests.post("https://<username>-<repo_id>.hf.space/v1/rental", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+    response = requests.post("https://Santhu976-ProdStoreSalesTotalPredictionBackend.hf.space/v1/sales", json=input_data.to_dict(orient='records')[0])
+    if response.status_code == 200:
+        prediction = response.json()['Predicted Price']
+        st.success(f"Predicted Product_Store_Sales_Total: {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://Santhu976-ProdStoreSalesTotalPredictionBackend.hf.space/v1/salesbatch", 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