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Dockerfile

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+# Use Python base image
+FROM python:3.10-slim
+
+# Set working directory
+WORKDIR /app
+
+# Copy all files into the container
+COPY . /app
+
+# Install dependencies
+RUN pip install --upgrade pip && \
+    pip install -r requirements.txt
+
+# Expose port for Streamlit
+EXPOSE 7860
+
+# Run Streamlit app
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]

app.py

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+
+# import
+import streamlit as st
+import pandas as pd
+import requests
+
+# Streamlit UI
+st.title("SuperKart Sales Prediction App")
+st.write("Predict store sales based on product and store attributes.")
+
+# Numerical Input fields
+product_weight = st.number_input("Product Weight", min_value=0.0, step=0.1)
+product_allocated_area = st.number_input("Product Allocated Area", min_value=0.0, step=0.1)
+product_mrp = st.number_input("Product MRP", min_value=0.0, step=0.1)
+store_age = st.number_input("Store Age (in years)", min_value=0, step=1)
+
+# Categorical inputs with options adapted from your data
+product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar", "reg"])
+product_type = st.selectbox(
+    "Product Type",['Frozen Foods', 'Dairy', 'Canned', 'Baking Goods', 'Health and Hygiene',
+     'Snack Foods', 'Meat', 'Household', 'Hard Drinks', 'Fruits and Vegetables',
+     'Breads', 'Soft Drinks', 'Breakfast', 'Others', 'Starchy Foods', 'Seafood'])
+store_type = st.selectbox("Store Type",['Supermarket Type2', 'Departmental Store', 'Supermarket Type1', 'Food Mart'])
+store_size = st.selectbox("Store Size (1=Small, 2=Medium, 3=Large)",[1, 2, 3])
+store_location_city_type = st.selectbox("Store Location City Type (1=Tier 1, 2=Tier 2, 3=Tier 3)",[1, 2, 3])
+
+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_Size': store_size,
+    'Store_Location_City_Type': store_location_city_type,
+    'Store_Type': store_type,
+    'Store_Age': store_age
+}])
+
+
+# Predict button
+if st.button("Predict"):
+    try:
+        response = requests.post(
+            "https://Vaddiritz-SuperKartBackend.hf.space/v1/salesprice",
+            json=input_data.to_dict(orient='records')[0]
+        )
+        if response.status_code == 200:
+            prediction = response.json().get("Predicted Price", "No prediction returned")
+            st.success(f"Predicted Sales Price: {prediction}")
+        else:
+            st.error("Error making prediction.")
+            st.text(response.text)
+    except Exception as e:
+        st.error(f"Exception occurred: {e}")
+
+# ----------------- Batch Prediction -----------------
+st.subheader("Batch Prediction")
+
+uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])
+
+if uploaded_file is not None:
+    if st.button("PredictBatch"):
+        try:
+            files = {"file": (uploaded_file.name, uploaded_file, "text/csv")}
+            response = requests.post(
+                "https://Vaddiritz-SuperKartBackend.hf.space/v1/salespricebatch",
+                files=files
+            )
+            if response.status_code == 200:
+                predictions = response.json()
+                st.success("Batch predictions completed!")
+
+                # Convert to DataFrame and display
+                df_predictions = pd.DataFrame(predictions)
+                st.dataframe(df_predictions)
+
+                # Download button
+                csv = df_predictions.to_csv(index=False).encode('utf-8')
+                st.download_button(
+                    label="Download Predictions as CSV",
+                    data=csv,
+                    file_name="SuperKart_Predicted_Sales.csv",
+                    mime="text/csv"
+                )
+            else:
+                st.error("Error making batch prediction.")
+                st.text(response.text)
+        except Exception as e:
+            st.error(f"Exception occurred: {e}")

requirements.txt

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+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+Werkzeug==2.2.2
+flask==2.2.2
+gunicorn==20.1.0
+requests==2.28.1
+streamlit==1.43.2
+flask-cors==3.0.10