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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://SujayAery-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://SujayAery-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}") | |