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Dockerfile

@@ -4,17 +4,13 @@ FROM python:3.9-slim
 # Set the working directory inside the container to /app
 WORKDIR /app
 
-# Copy the requirements file first to leverage Docker cache
-COPY requirements.txt .
-
-# Install Python dependencies
-RUN pip install --no-cache-dir -r requirements.txt
-
-# Copy the rest of the application code
+# Copy all files from the current directory on the host to the container's /app directory
 COPY . .
 
-# Expose the port the Flask app runs on
-EXPOSE 5000
+# Install Python dependencies listed in requirements.txt
+RUN pip3 install -r requirements.txt
+
+# 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"]
 
-# Define the command to run the Flask app
-CMD ["python", "app.py"]
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

@@ -1,33 +1,56 @@
-import joblib
-from flask import Flask, request, jsonify
+import streamlit as st
+import requests
 import pandas as pd
+import json
+
+st.title("SuperKart Sales Forecasting")
+
+st.write("Enter the product and store details to get a sales forecast.")
+
+# Input fields for features (based on the columns in your X_train)
+product_weight = st.number_input("Product Weight", min_value=0.1)
+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', 'Household', 'Snack Foods', 'Fruits and Vegetables', 'Meat', 'Soft Drinks', 'Breads', 'Hard Drinks', 'Others', 'Starchy Foods', 'Breakfast', 'Seafood'])
+product_mrp = st.number_input("Product MRP", min_value=0.1)
+store_id = st.selectbox("Store ID", ['OUT004', 'OUT003', 'OUT001', 'OUT002'])
+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'])
+store_age = st.number_input("Store Age (Years)", min_value=0)
+
+# Create a dictionary with the input data
+input_data = {
+    '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_Size': store_size,
+    'Store_Location_City_Type': store_location_city_type,
+    'Store_Type': store_type,
+    'Store_Age': store_age
+}
+
+# Create a DataFrame from the input data
+input_df = pd.DataFrame([input_data])
+
+# Button to trigger prediction
+if st.button("Predict Sales"):
+    # Convert DataFrame to JSON
+    json_data = json.dumps(input_df.to_dict(orient='records'))
+
+    # Replace with your backend Space URL
+    backend_url = "abdulrahman305/SuperKart"
 
-app = Flask(__name__)
-
-# Load the serialized model
-model = joblib.load('best_regression_model.pkl')
-
-@app.route('/')
-def home():
-    return "SuperKart Sales Forecasting Backend is running!"
-
-@app.route('/predict', methods=['POST'])
-def predict():
     try:
-        # Get data from the request (assuming JSON format)
-        data = request.get_json(force=True)
-        df_pred = pd.DataFrame(data)
-
-        # Make predictions
-        predictions = model.predict(df_pred)
-
-        # Return predictions as JSON
-        return jsonify(predictions.tolist())
+        response = requests.post(backend_url, json=json.loads(json_data))
 
+        if response.status_code == 200:
+            predictions = response.json()
+            st.success(f"Predicted Sales: {predictions[0]:.2f}")
+        else:
+            st.error(f"Error predicting sales: {response.text}")
     except Exception as e:
-        return jsonify({'error': str(e)})
-
-if __name__ == '__main__':
-    # Run the Flask app
-    # Using 0.0.0.0 to make it accessible externally within the Docker container
-    app.run(host='0.0.0.0', port=5000)
+        st.error(f"An error occurred: {e}")

requirements.txt

@@ -1,4 +1,3 @@
-flask==3.0.3
+streamlit==1.36.0
+requests==2.32.3
 pandas==2.2.2
-scikit-learn==1.6.1
-joblib==1.4.2