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app.py

@@ -1,46 +1,47 @@
-import streamlit as st
-
-st.title("SuperKart Sales Forecast")
-
-st.write("Enter the product and store details to get a sales forecast.")
-
-# Add input fields for the features
-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 Weight", min_value=0.0016)
-Product_Type = st.selectbox("Product Sugar Content", ['Frozen Foods','Dairy','Canned','Baking Goods','Health and Hygiene','Snack Foods','Household','Breakfast'])
-Product_MRP =  st.number_input("Product_MRP", min_value=5.0)
-Store_Id   = st.selectbox("Product Sugar Content", ['OUT001', 'OUT002', 'OUT003','OUT004'])
-Store_Establishment_Year = st.selectbox["Store_Establishment_Year",'1987','1998','1999','2009']
-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_Location_City_Type",'Supermarket Type1','Food Mart','Departmental Store','Supermarket Type2']
-
-# Button to trigger prediction
-if st.button("Get Forecast"):
-#     # Prepare data for the API
-#     # This should be a dictionary or list of dictionaries matching the expected input format of your Flask API
-     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_Establishment_Year' : Store_Establishment_Year,
-         'Store_Size' : Store_Size,
-         'Store_Location_City_Type' : Store_Location_City_Type,
-         'Store_Type' :Store_Type
-     }
-
-#     # Send data to the backend API
-#     # Replace with the actual URL of your deployed Flask API
-     backend_url = "https://vamshf/Docker-gl-project.hf.space/predict"
-
-     try:
-         response = requests.post(backend_url, json=[input_data]) # Send as a list if your API expects a list
-         predictions = response.json()
-
-         st.success(f"Predicted Sales: {predictions[0]:.2f}") # Assuming the API returns a list with one prediction
-     except Exception as e:
-         st.error(f"Error getting forecast: {e}")
+from flask import Flask,jsonify,request
+
+# Initialize Flask app
+superKart_Sales_forecast = Flask("SuperKart Sales Forecast")
+
+# Load the serialized model
+try:
+    loaded_model = joblib.load('tuned_random_forest_model.pkl')
+    print("Model loaded successfully!")
+except Exception as e:
+    print(f"Error loading model: {e}")
+    loaded_model = None # Set model to None if loading fails
+
+
+@superKart_Sales_forecast.route('/predict', methods=['POST'])
+def predict():
+    if loaded_model is None:
+        return jsonify({'error': 'Model not loaded'}), 500
+
+    try:
+        # Get data from the request
+        data = request.get_json(force=True)
+        # Convert the incoming data to a pandas DataFrame
+        # Assuming the incoming data is a list of dictionaries, where each dictionary is a row
+        # The columns should match the features used during training (excluding the target)
+        input_df = pd.DataFrame(data)
+
+        # Ensure the columns are in the same order as the training data
+        # You might need to store the order of columns from X_train during training
+        # For now, assuming input_df columns match X_train columns
+        # A more robust solution would involve saving and loading the column order
+        # For demonstration, let's assume the column order is consistent
+
+        # Make predictions
+        predictions = loaded_model.predict(input_df)
+
+        # Convert predictions to a list and return as JSON
+        return jsonify(predictions.tolist())
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 400
+
+# To run the Flask superKart_Sales_forecast (for local testing)
+#if __name__ == '__main__':
+#     # This will run the server locally on port 5000
+#     # In a production environment, you would use a production-ready server like Gunicorn or uWSGI
+#      superKart_Sales_forecast.run(debug=True, host='0.0.0.0', port=5000)