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backend_files/Dockerfile

@@ -13,4 +13,4 @@ RUN pip install --no-cache-dir --upgrade -r requirements.txt
 # - `-w 4`: Uses 4 worker processes for handling requests
 # - `-b 0.0.0.0:7860`: Binds the server to port 7860 on all network interfaces
 # - `app:app`: Runs the Flask app (assuming `app.py` contains the Flask instance named `app`)
-CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:rental_price_predictor_api"]
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:superkart_sales_api"]

backend_files/app.py

@@ -20,75 +20,75 @@ def home():
     """
     return "Welcome to the SuperKart Sales Prediction API!"
 
-# Define an endpoint for single sales prediction (POST request)
+# Endpoint for Single Prediction
+# -------------------------------
 @superkart_sales_api.post('/v1/sales')
 def predict_sales():
     """
-    This function handles POST requests to the '/v1/sales' endpoint.
-    It expects a JSON payload containing product and store details
-    and returns the predicted sales as a JSON response.
+    Predict sales for a single product-outlet combination
     """
-    # Get the JSON data from the request body
-    product_data = request.get_json()
-
-    # Extract relevant features from the JSON data
-    sample = {
-        'Product_Weight': product_data['Product_Weight'],
-        'Product_Allocated_Area': product_data['Product_Allocated_Area'],
-        'Product_MRP': product_data['Product_MRP'],
-        'Store_Establishment_Year': product_data['Store_Establishment_Year'],
-        'Product_Sugar_Content': product_data['Product_Sugar_Content'],
-        'Product_Type': product_data['Product_Type'],
-        'Store_Size': product_data['Store_Size'],
-        'Store_Location_City_Type': product_data['Store_Location_City_Type'],
-        'Store_Type': product_data['Store_Type']
-    }
-
-    # Convert the extracted data into a Pandas DataFrame
-    input_data = pd.DataFrame([sample])
-
-    # Make prediction
-    predicted_sales = model.predict(input_data)[0]
-
-    # Convert predicted_sales to Python float
-    predicted_sales = round(float(predicted_sales), 2)
-
-    # Return the prediction as JSON
-    return jsonify({'Predicted Sales (units)': predicted_sales})
-
-
-# Define an endpoint for batch prediction (POST request)
+    try:
+        # Get JSON data from request
+        data = request.get_json()
+
+        # Extract relevant features
+        sample = {
+            'Product_Weight': data['Product_Weight'],            
+            'Product_Allocated_Area': data['Product_Allocated_Area'],
+            'Product_MRP': data['Product_MRP'],
+            'Store_Establishment_Year': data['Store_Establishment_Year'],
+            'Product_Sugar_Content': data['Product_Sugar_Content'],
+            'Store_Size': data['Store_Size'],
+            'Store_Location_City_Type': data['Store_Location_City_Type'],
+            'Store_Type': data['Store_Type'],
+            'Product_Type': data['Product_Type']
+
+        }
+          
+        # Convert to DataFrame
+        input_df = pd.DataFrame([sample])
+
+        # Make prediction
+        prediction = model.predict(input_df)[0]
+
+        # Convert to float and round
+        prediction = round(float(prediction), 2)
+
+        return jsonify({"Predicted Sales": prediction})
+
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+# -------------------------------
+# Endpoint for Batch Prediction
+# -------------------------------
 @superkart_sales_api.post('/v1/salesbatch')
 def predict_sales_batch():
     """
-    This function handles POST requests to the '/v1/salesbatch' endpoint.
-    It expects a CSV file containing product details for multiple products
-    and returns the predicted sales as a dictionary in the JSON response.
+    Predict sales for multiple rows from a CSV file
     """
-    # Get the uploaded CSV file from the request
-    file = request.files['file']
-
-    # Read the CSV file into a Pandas DataFrame
-    input_data = pd.read_csv(file)
+    try:
+        # Get uploaded file
+        file = request.files['file']
 
-    # Make predictions for all rows in the DataFrame
-    predicted_sales = model.predict(input_data).tolist()
+        # Read into DataFrame
+        input_df = pd.read_csv(file)
 
-    # Round and convert to float
-    predicted_sales = [round(float(sale), 2) for sale in predicted_sales]
+        # Make predictions
+        predictions = model.predict(input_df).tolist()
+        predictions = [round(float(p), 2) for p in predictions]
 
-    # If the CSV has an 'id' column, use it as keys; else just index
-    if 'id' in input_data.columns:
-        ids = input_data['id'].tolist()
-    else:
-        ids = list(range(1, len(predicted_sales) + 1))
+        # Return predictions in a dict format with row index as key
+        output_dict = {str(i): predictions[i] for i in range(len(predictions))}
 
-    output_dict = dict(zip(ids, predicted_sales))
+        return jsonify(output_dict)
 
-    # Return the predictions dictionary as a JSON response
-    return jsonify(output_dict)
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
 
-
-# Run the Flask application in debug mode if this script is executed directly
+# -------------------------------
+# Run App
+# -------------------------------
 if __name__ == '__main__':
-    superkart_sales_api.run(debug=True)
+    superkart_sales_api.run(debug=True, host="0.0.0.0", port=7860)
+

backend_files/superkart_sales_prediction_model_v1_0.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9fcee8aeaaff84451c98d1846e7b158bfaf565d370611ce5b29d254d1cc9fe7d
+size 207980

frontend_files/Dockerfile

@@ -11,6 +11,7 @@ COPY . .
 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"]
+#CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]
 
 # NOTE: Disable XSRF protection for easier external access in order to make batch predictions

frontend_files/app.py

@@ -1,4 +1,3 @@
-
 import streamlit as st
 import pandas as pd
 import requests
@@ -13,35 +12,42 @@ st.title("Superkart Sales Prediction App")
 # Section for online prediction
 st.subheader("Online Prediction")
 
-# Collect user input for product & store features
-product_weight = st.number_input("Product Weight (grams)", min_value=0.0, value=500.0, step=50.0)
-product_visibility = st.number_input("Product Visibility", min_value=0.0, max_value=1.0, step=0.01, value=0.05)
-product_mrp = st.number_input("Product MRP (₹)", min_value=0.0, value=120.0, step=1.0)
 
-outlet_establishment_year = st.number_input("Outlet Establishment Year", min_value=1950, max_value=2030, value=2000, step=1)
-outlet_size = st.selectbox("Outlet Size", ["Small", "Medium", "High"])
-outlet_location_type = st.selectbox("Outlet Location Type", ["Tier 1", "Tier 2", "Tier 3"])
-outlet_type = st.selectbox("Outlet Type", ["Grocery Store", "Supermarket Type1", "Supermarket Type2", "Supermarket Type3"])
+# Numeric inputs
+Product_Weight = st.number_input("Product Weight (in grams)", min_value=0.0, value=500.0)
+Product_Allocated_Area = st.number_input("Allocated Area (sq ft)", min_value=0.0, value=100.0)
+Product_MRP = st.number_input("Product MRP", min_value=0.0, value=50.0)
+Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1900, max_value=2025, value=2000)
+
+# Categorical inputs
+Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low", "Medium", "High"])
+Product_Type = st.selectbox("Product Type", ["Food", "Beverage", "Snack", "Other"])
+Store_Size = st.selectbox("Store Size", ["Small", "Medium", "Large"])
+Store_Location_City_Type = st.selectbox("City Type", ["Tier 1", "Tier 2", "Tier 3"])
+Store_Type = st.selectbox("Store Type", ["Mall", "Standalone", "Supermarket", "Other"])
+
+ 
 
 # Convert user input into a DataFrame
 input_data = pd.DataFrame([{
-    
-    'Product_Weight': product_weight,
-    'Product_Visibility': product_visibility,
-    'Product_MRP': product_mrp,
-    'Outlet_Establishment_Year': outlet_establishment_year,
-    'Outlet_Size': outlet_size,
-    'Outlet_Location_Type': outlet_location_type,
-    'Outlet_Type': outlet_type
+    'Product_Weight': Product_Weight,
+    'Product_Allocated_Area': Product_Allocated_Area,
+    'Product_MRP': Product_MRP,
+    'Store_Establishment_Year': Store_Establishment_Year,
+    'Product_Sugar_Content': Product_Sugar_Content,
+    'Product_Type': Product_Type,
+    'Store_Size': Store_Size,
+    'Store_Location_City_Type': Store_Location_City_Type,
+    'Store_Type': Store_Type
 }])
 
 # Make prediction when the "Predict" button is clicked
 if st.button("Predict Sales"):
     response = requests.post(
-        "https://<username>-Superkart_Docker_space.hf.space/v1/sales",
+        "https://Anusha3-Superkart-Backend-Docker-space.hf.space/v1/sales",
         json=input_data.to_dict(orient='records')[0]
     )  # Send data to backend API
-    
+
     if response.status_code == 200:
         prediction = response.json()['Predicted Sales']
         st.success(f"Predicted Sales: {prediction}")
@@ -58,13 +64,14 @@ uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["
 if uploaded_file is not None:
     if st.button("Predict Batch Sales"):
         response = requests.post(
-            "https://<username>-Superkart_Docker_space.hf.space/v1/salesbatch",
-            files={"file": uploaded_file}
-        )  # Send file to backend API
-        
+            "https://Anusha3-Superkart-Backend-Docker-space.hf.space/v1/salesbatch",  files={"file": uploaded_file}   )  # Send file to backend 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.")
+
+
+