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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"]

app.py

@@ -0,0 +1,91 @@
+import streamlit as st
+import requests
+import json
+import pandas as pd
+
+# Define the backend API URL
+# Replace with the actual URL of your deployed backend API
+BACKEND_API_URL_SINGLE = "YOUR_BACKEND_API_URL/predict_single"
+BACKEND_API_URL_BATCH = "YOUR_BACKEND_API_URL/predict_batch"
+
+
+st.title("SuperKart Sales Forecasting")
+
+st.write("This application forecasts the sales revenue for product-store combinations.")
+
+# Option to choose between single prediction and batch prediction
+prediction_mode = st.radio("Select Prediction Mode:", ("Single Prediction", "Batch Prediction"))
+
+if prediction_mode == "Single Prediction":
+    st.header("Predict Sales for a Single Item")
+
+    # Input fields for product and store details
+    product_id = st.text_input("Product ID")
+    product_weight = st.number_input("Product Weight", min_value=0.0)
+    product_sugar_content = st.selectbox("Product Sugar Content", ['Low Sugar', 'Regular', 'No Sugar'])
+    product_allocated_area = st.number_input("Product Allocated Area", min_value=0.0)
+    product_type = st.selectbox("Product Type", ['Frozen Foods', 'Dairy', 'Canned', 'Baking Goods', 'Health and Hygiene', 'Snack Foods', 'Meat', 'Hard Drinks', 'Fruits and Vegetables', 'Breads', 'Soft Drinks', 'Breakfast', 'Others', 'Starchy Foods', 'Seafood', 'Household'])
+    product_mrp = st.number_input("Product MRP", min_value=0.0)
+    store_id = st.selectbox("Store ID", ['OUT004', 'OUT003', 'OUT001', 'OUT002'])
+    store_establishment_year = st.number_input("Store Establishment Year", min_value=1900, max_value=2024)
+    store_size = st.selectbox("Store Size", ['Medium', 'High', 'Small'])
+    store_location_city_type = st.selectbox("Store Location City Type", ['Tier 2', 'Tier 1', 'Tier 3'])
+    store_type = st.selectbox("Store Type", ['Supermarket Type2', 'Departmental Store', 'Supermarket Type1', 'Food Mart'])
+
+
+    if st.button("Predict Sales"):
+        # Prepare data for the API request
+        input_data = {
+            "Product_Id": product_id,
+            "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
+        }
+
+        # Send POST request to the backend API
+        response = requests.post(BACKEND_API_URL_SINGLE, json=input_data)
+
+        # Display the prediction result
+        if response.status_code == 200:
+            prediction = response.json()['predicted_sales']
+            st.success(f"Predicted Sales: {prediction:.2f}")
+        else:
+            st.error(f"Error: {response.status_code} - {response.text}")
+
+elif prediction_mode == "Batch Prediction":
+    st.header("Predict Sales for a Batch of Items")
+    st.write("Upload a CSV file with product and store details.")
+
+    uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+
+    if uploaded_file is not None:
+        try:
+            # Read the uploaded CSV file into a DataFrame
+            input_df = pd.read_csv(uploaded_file)
+            st.write("Uploaded Data:")
+            st.dataframe(input_df)
+
+            if st.button("Predict Sales (Batch)"):
+                 # Send POST request to the backend API with the CSV file
+                files = {'file': uploaded_file.getvalue()}
+                response = requests.post(BACKEND_API_URL_BATCH, files=files)
+
+                # Display the prediction result
+                if response.status_code == 200:
+                    predictions = response.json()['predicted_sales']
+                    # Display predictions in a DataFrame
+                    predictions_df = pd.DataFrame({'Predicted_Sales': predictions})
+                    st.write("Predicted Sales:")
+                    st.dataframe(predictions_df)
+                else:
+                    st.error(f"Error: {response.status_code} - {response.text}")
+
+        except Exception as e:
+            st.error(f"Error processing file: {e}")

requirements.txt

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