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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("Store Sales Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect user input for property features
+Product_Sugar_Content = st.selectbox("Product_Sugar_Content" , ["Low Sugar", "Regular", "No Sugar"])
+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_Id = st.selectbox("Store_Id" , ["OUT001","OUT002","OUT003","OUT004"])
+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"])
+Product_Weight = st.number_input("Product_Weight", min_value=0.00, max_value=100.00, step=0.01, value=0.0)
+Product_Allocated_Area = st.number_input("Product_Allocated_Area", min_value=0.000, max_value=1.000, step=0.001, value=0.000)
+Product_MRP = st.number_input("Product_Weight", min_value=0.0, step=0.01, value=0.0)
+Store_Establishment_Year = st.number_input("Store_Establishment_Year", min_value=1800, max_value=3000, step=1, value=1900)
+
+
+# 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_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
+}])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    response = requests.post("https://adityasharma0511-StoreSalesPredictionBackend.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 Store Sales (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://adityasharma0511-StoreSalesPredictionBackend.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