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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 Sales Forecast")
+
+# Section for online prediction
+st.subheader("Sales Prediction")
+
+# Collect user input for property features
+product_weight = st.number_input(("Product Weight")
+product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+product_allocated_area = st.number_input(("Product Allocated Area")
+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")
+store_establishment_year = st.number_input("Store Establishment Year", min_value=1900, step=1)
+store_size = st.number_input("Store Size", min_value=0, step=1, value=1)
+store_location_city_type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
+store_type = st.selectbox("Store Type", ["Food Mart", "Departmental Store", "Supermarket Type1", "Supermarket Type2" ])
+
+
+# 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://aks2022-superkartsalesforecastbackend.hf.space/v1/sales", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+    if response.status_code == 200:
+        prediction = response.json()['Predicted Price (in dollars)']
+        st.success(f"Predicted Rental Price (in dollars): {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://aks2022-superkartsalesforecastbackend.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
+numpy
+flask