Upload Streamlit app and model

Dockerfile

@@ -1,21 +1,14 @@
+# Use a minimal base image with Python 3.9 installed
 FROM python:3.9-slim
 
+# Set the working directory inside the container to /app
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    software-properties-common \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
 
+# Install Python dependencies listed in requirements.txt
 RUN pip3 install -r requirements.txt
 
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
-
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# Define the command to run the Streamlit app on port 8501 and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

SuperKart_Mode_v2_0.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76964249af602d97766de8e0ae7aa9464ff20afeeb16ca2e4f3ff363288740c3
+size 405737

streamlit_app.py

@@ -1,52 +1,44 @@
-
 import streamlit as st
 import pandas as pd
 import joblib
 
 # Load trained model pipeline
-model = joblib.load('SuperKart_Mode_v1_0.joblib')
-
-# Streamlit app title
-st.title("🛒 Product Store Sales Prediction App")
-st.markdown("Enter product and store details below to predict sales.")
-
-# Input form
-with st.form("prediction_form"):
-    product_weight = st.number_input("Product Weight (in kg)", min_value=1.0, max_value=50.0, value=13.5)
-    product_allocated_area = st.number_input("Allocated Area (0 to 1)", min_value=0.001, max_value=1.0, value=0.08)
-    product_mrp = st.number_input("Product MRP (₹)", min_value=1.0, max_value=1000.0, value=250.75)
-    store_age = st.number_input("Store Age (in years)", min_value=1, max_value=100, value=20)
-
-    product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar", "High Sugar"])
-    product_type = st.selectbox("Product Type", [
-        "Meat", "Snack Foods", "Hard Drinks", "Dairy", "Canned", "Soft Drinks",
-        "Health and Hygiene", "Baking Goods", "Bread", "Breakfast", "Frozen Foods",
-        "Fruits and Vegetables", "Household", "Seafood", "Starchy Foods", "Others"
-    ])
-    store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
-    store_location = st.selectbox("City Tier", ["Tier 1", "Tier 2", "Tier 3"])
-    store_type = st.selectbox("Store Type", ["Departmental Store", "Supermarket Type1", "Supermarket Type2", "Food Mart"])
-
-    submit = st.form_submit_button("Predict Sales")
-
-# Prediction logic
-if submit:
-    input_dict = {
+model = joblib.load('SuperKart_Mode_v2_0.joblib')  # Update the path if needed
+
+# App title
+st.title("Sales Predictor")
+
+# Sidebar inputs
+st.sidebar.header("Input Product & Store Details")
+
+product_weight = st.sidebar.slider("Product Weight (kg)", 1.0, 50.0, 13.5)
+allocated_area = st.sidebar.slider("Allocated Area (0 to 1)", 0.001, 1.0, 0.08)
+product_mrp = st.sidebar.slider("Product MRP (₹)", 1.0, 1000.0, 250.75)
+store_age = st.sidebar.slider("Store Age (years)", 1, 100, 20)
+
+sugar_content = st.sidebar.selectbox("Sugar Content", ["Low Sugar", "Regular", "No Sugar", "High Sugar"])
+product_type = st.sidebar.selectbox("Product Type", [
+    "Meat", "Snack Foods", "Hard Drinks", "Dairy", "Canned", "Soft Drinks",
+    "Health and Hygiene", "Baking Goods", "Bread", "Breakfast", "Frozen Foods",
+    "Fruits and Vegetables", "Household", "Seafood", "Starchy Foods", "Others"
+])
+store_size = st.sidebar.selectbox("Store Size", ["Small", "Medium", "High"])
+city_tier = st.sidebar.selectbox("City Tier", ["Tier 1", "Tier 2", "Tier 3"])
+store_type = st.sidebar.selectbox("Store Type", ["Departmental Store", "Supermarket Type1", "Supermarket Type2", "Food Mart"])
+
+# Predict button
+if st.sidebar.button("Predict Sales"):
+    input_data = pd.DataFrame({
         "Product_Weight": [product_weight],
-        "Product_Allocated_Area": [product_allocated_area],
+        "Product_Allocated_Area": [allocated_area],
         "Product_MRP": [product_mrp],
         "Store_Age": [store_age],
-        "Product_Sugar_Content": [product_sugar_content],
+        "Product_Sugar_Content": [sugar_content],
         "Product_Type": [product_type],
         "Store_Size": [store_size],
-        "Store_Location_City_Type": [store_location],
+        "Store_Location_City_Type": [city_tier],
         "Store_Type": [store_type]
-    }
-
-    input_df = pd.DataFrame(input_dict)
-
-    # Predict using loaded model
-    prediction = model.predict(input_df)[0]
+    })
 
-    # Show result
-    st.success(f"📈 Predicted Product Store Sales: ₹{prediction:,.2f}")
+    prediction = model.predict(input_data)[0]
+    st.success(f"Predicted Sales: Rs{prediction:,.2f}")