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Dockerfile

@@ -1,20 +1,16 @@
-FROM python:3.13.5-slim
+# 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 \
-    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
+# 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"]
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

@@ -0,0 +1,57 @@
+
+import streamlit as st
+import requests
+
+st.title("Super Kart Sales Predictor")
+
+# Input fields for product and store data
+Product_Weight = st.slider("Product Weight", min_value=0.0, value=12.0, max_value = 22.0)
+Product_MRP = st.slider("Product MRP", min_value=30.0, value=145.0, max_value=260.0)
+Product_Allocated_Area = st.slider("Product Allocated Area", min_value=0.0, value=0.05, max_value=0.3)
+Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+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", ["Departmental Store	", "Supermarket Type1", "Supermarket Type2", "Food Mart"])
+Store_Age_Years = st.slider("Store Age (Years)", min_value=0, value=15, max_value=40)
+Product_Id_prefix = st.selectbox("Product ID Prefix", ["FD", "DR", "NC"])
+Product_FD_perishable = st.selectbox("Product FD Perishable flag", ["Perishables", "Non Perishables"])
+
+product_data = {
+    "Product_Weight": Product_Weight,
+    "Product_MRP": Product_MRP,
+    "Product_Allocated_Area": Product_Allocated_Area,
+    "Product_Sugar_Content": Product_Sugar_Content,
+    "Store_Size": Store_Size,
+    "Store_Location_City_Type": Store_Location_City_Type,
+    "Store_Type": Store_Type,
+    "Store_Age_Years": Store_Age_Years,
+    "Product_Id_prefix": Product_Id_prefix,
+    "Product_FD_perishable": Product_FD_perishable,
+}
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict", type='primary'):
+    response = requests.post("https://AlbertoNuin-SuperKartBackend.hf.space/v1/predict", json=product_data)
+    if response.status_code == 200:
+        result = response.json()
+        predicted_sales = result["Sales"]
+        st.write(f"Predicted Product Store Sales Total: {predicted_sales:.2f}")
+    else:
+        st.error("Error in API request")
+
+# 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://AlbertoNuin-SuperKartBackend.hf.space/v1/batch", 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

@@ -1,3 +1,3 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+requests==2.28.1
+streamlit==1.43.2