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Dockerfile

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+FROM python:3.9-slim
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY app.py .
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]

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("Product Store Sales Revenue Predictor")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+   
+# Collect user input for property features
+ProductWeight = st.number_input("Product_Weight", min_value=1.00, value=1.00)
+ProductAllocatedArea = st.number_input("Product_Allocated_Area", min_value=0.0001, value=0.0001)
+ProductMRP = st.number_input("Product_MRP", min_value=1.00,value=1.00)
+StoreEstablishmentYear = st.number_input("Store_Establishment_Year", min_value=1980,value=1990)
+ProductSugarContent = st.selectbox("Product_Sugar_Content", ["Low Sugar", "Regular", "No Sugar"])
+ProductType = 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"])
+StoreId = st.selectbox("Store_Id", ["OUT001", "OUT002", "OUT003","OUT004"])
+StoreSize = st.selectbox("Store_Size", ["Small", "Medium", "High"])
+StoreLocationCityType = st.selectbox("Store_Location_City_Type", ["Tier 1", "Tier 2", "Tier 3"])
+StoreType = st.selectbox("Store_Type", ["Departmental Store", "Food Mart", "Supermarket Type1","Supermarket Type2"])
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+    'Product_Weight': ProductWeight,
+    'Product_Allocated_Area': ProductAllocatedArea,
+    'Product_MRP': ProductMRP,
+    'Store_Establishment_Year': StoreEstablishmentYear,
+    'Product_Sugar_Content': ProductSugarContent,
+    ##'instant_bookable': 'f' if instant_bookable=="False" else "t",  # Convert to 't' or 'f'
+    'Product_Type': ProductType,
+    'Store_Id': StoreId,
+    'Store_Size': StoreSize,
+    'Store_Location_City_Type': StoreLocationCityType,
+    'Store_Type': StoreType
+}])
+
+# Make prediction when the "Predict" button is clicked
+# if st.button("Predict"):
+#     response = requests.post("https://MainiSandeep1987-SuperKartBackEnd.hf.space/v1/salesTotal", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+#     if response.status_code == 200:
+#         prediction = response.json()['Predicted Sales Revenue Price']
+#         st.success(f"Predicted Sales Revenue : {prediction}")
+#     else:
+#         st.error("Error making prediction.")
+
+if st.button("Predict"):
+    try:
+        response = requests.post(
+            "https://MainiSandeep1987-SuperKartBackEnd.hf.space/v1/salesTotal",
+            json=input_data.to_dict(orient='records')[0]
+        )  # Send data to Flask API
+        
+        if response.status_code == 200:
+            prediction = response.json().get('Predicted Sales Revenue Price', "Prediction not found")
+            st.success(f"Predicted Sales Revenue : {prediction}")
+        else:
+            st.error(f"Error {response.status_code}: {response.text}")
+
+    except requests.exceptions.RequestException as e:
+        st.error(f"Request failed: {str(e)}")        

requirements.txt

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+pandas==2.2.2
+requests==2.28.1
+streamlit==1.43.2