added files

file.py

@@ -0,0 +1,116 @@
+print("heelo world!!")
+
+
+#---------------My creation of starbucks drink recommendation system----------------------#
+
+
+#---------------Importing all necessary libraries ----------------------#
+
+
+from typing import List
+from scipy.spatial import distance
+import pandas as pd 
+#from pyscript import Element
+from scipy.spatial.distance import euclidean
+
+
+
+#---------------Initial setup and loading of data ----------------------#
+
+file_path = 'starbucks_drinks.csv'
+sbdrinks_data = pd.read_csv(file_path)
+
+## i renamed the column coz its doesnt name sense for a cloumn name to be "490"
+
+sbdrinks_data.rename(columns={'490': 'Drink_name'}, inplace=True)
+
+## creating a new column Drink_id  and adding it as first column 
+
+sbdrinks_data['Drink_id'] = range(1, len(sbdrinks_data) + 1)
+columns = ['Drink_id'] + sbdrinks_data.columns[:-1].tolist()
+sbdrinks_data = sbdrinks_data[columns]
+print(sbdrinks_data.dtypes)
+
+
+#---------------Turning data into float data types ----------------------#
+
+columns_to_convert = sbdrinks_data.columns.difference(['Drink_id', 'Drink_name'])
+sbdrinks_data[columns_to_convert] = sbdrinks_data[columns_to_convert].astype(float)
+
+# Print the data types after conversion
+print("\nData types after conversion:")
+print(sbdrinks_data.dtypes)
+
+# Verify the DataFrame structure
+# print("\nDataFrame:")
+# print(sbdrinks_data.head())
+drinks_aslist = sbdrinks_data['Drink_name']
+
+
+#---------------Main Logic ----------------------#
+
+
+def mean_selected_drink(s_drinks):
+    mean_vector = s_drinks.drop(columns=['Drink_id', 'Drink_name']).mean().values
+    return mean_vector
+
+# Function to find closest drinks based on mean vector
+def find_closest_drinks(mean_vector, all_drinks_data, n_recommendations=5):
+    # Calculate Euclidean distances
+    all_drinks_data['distance'] = all_drinks_data.apply(lambda row: euclidean(mean_vector, row.drop(['Drink_id', 'Drink_name'])), axis=1)
+    # Sort drinks by distance and get top recommendations
+    closest_drinks = all_drinks_data.sort_values(by='distance').head(n_recommendations)
+    return closest_drinks[['Drink_id', 'Drink_name', 'distance']]
+
+# Function to get recommendations based on selected drink names
+def get_drink_recommendations(selected_drinks_names, all_drinks_data, include_all=True, include_coffee=True, include_tea=True, include_neither=False, include_hot=True, include_cold=True, include_frozen=True, n_recommendations=5):
+    selected_drinks = all_drinks_data[all_drinks_data['Drink_name'].isin(selected_drinks_names)]
+    
+    if selected_drinks.empty:
+        print("No drinks selected")
+        return pd.DataFrame(columns=['Drink_id', 'Drink_name', 'distance'])
+    
+    # Calculating mean vector of selected drinks
+    mean_vector = mean_selected_drink(selected_drinks)
+
+    
+    #applying Filters on all_drinks_data based on user's preference
+    filtered_drinks = pd.DataFrame(columns=all_drinks_data.columns)
+    
+    for index, row in all_drinks_data.iterrows():
+        if include_all:
+            filtered_drinks = pd.concat([filtered_drinks, pd.DataFrame([row])], ignore_index=True, sort=False)
+        else:
+            if include_coffee and row['is_coffee'] == 1:
+                filtered_drinks = pd.concat([filtered_drinks, pd.DataFrame([row])], ignore_index=True, sort=False)
+            if include_tea and row['is_tea'] == 1:
+                filtered_drinks = pd.concat([filtered_drinks, pd.DataFrame([row])], ignore_index=True, sort=False)
+            if include_neither and row['is_not_coffee_or_tea'] == 1:
+                filtered_drinks = pd.concat([filtered_drinks, pd.DataFrame([row])], ignore_index=True, sort=False)
+            if include_hot and row['is_hot'] == 1:
+                filtered_drinks = pd.concat([filtered_drinks, pd.DataFrame([row])], ignore_index=True, sort=False)
+            if include_cold and row['is_cold'] == 1:
+                filtered_drinks = pd.concat([filtered_drinks, pd.DataFrame([row])], ignore_index=True, sort=False)
+            if include_frozen and row['is_frozen'] == 1:
+                filtered_drinks = pd.concat([filtered_drinks, pd.DataFrame([row])], ignore_index=True, sort=False)
+                
+    if filtered_drinks.empty:
+        print("No drinks matched the filters.")
+        return pd.DataFrame(columns=['Drink_id', 'Drink_name', 'distance'])
+
+    recommendations = find_closest_drinks(mean_vector,filtered_drinks, n_recommendations)
+    print(recommendations)
+    print(type(recommendations))
+    return recommendations
+
+# Example usage
+
+
+
+#--------------- Functions for clearing active_drinks and toggling drinks ----------------------#
+
+ 
+
+
+
+

main.py

@@ -1,9 +1,63 @@
-from flask import Flask
-from flask import render_template
+from flask import Flask, render_template, request,jsonify 
+import pandas as pd
+from file import get_drink_recommendations, sbdrinks_data
+
 app = Flask(__name__)
 
+# Assuming sbdrinks_data is loaded or imported correctly
+drinks_aslist = sbdrinks_data['Drink_name'].tolist()
 
 @app.route('/')
-@app.route('/<name>')
-def hello(name=None):
-    return render_template('hello.html', name=name)
+def index():
+    drinks_aslist = sbdrinks_data['Drink_name'].tolist()
+    return render_template('index.html', drinks=drinks_aslist)
+
+@app.route('/recommend', methods=['POST'])
+def recommend():
+    try:
+        selected_drinks_names = request.form.getlist('selected_drinks')
+
+        include_all = request.form.get('include_all') == '1'
+        include_coffee = request.form.get('include_coffee') == '1'
+        include_tea = request.form.get('include_tea') == '1'
+        include_neither = request.form.get('include_neither') == '1'
+        include_hot = request.form.get('include_hot') == '1'
+        include_cold = request.form.get('include_cold') == '1'
+        include_frozen = request.form.get('include_frozen') == '1'
+        n_recommendations = int(request.form.get('n_recommendations', 5))
+
+        print("Include All:", include_all)
+        print("Include Coffee:", include_coffee)
+        print("Include Tea:", include_tea)
+        print("Include Neither:", include_neither)
+        print("Include Hot:", include_hot)
+        print("Include Cold:", include_cold)
+        print("Include Frozen:", include_frozen)
+        print("Number of Recommendations:", n_recommendations)
+
+        # Call your recommendation function
+        recommendations_df = get_drink_recommendations(
+            selected_drinks_names,
+            sbdrinks_data,
+            include_all=include_all,
+            include_coffee=include_coffee,
+            include_tea=include_tea,
+            include_neither=include_neither,
+            include_hot=include_hot,
+            include_cold=include_cold,
+            include_frozen=include_frozen,
+            n_recommendations=n_recommendations
+        )
+
+        print(recommendations_df)
+
+        # Convert to list of dictionaries for JSON response
+        recommendations_list = recommendations_df.to_dict(orient='records')
+
+        return jsonify(recommendations=recommendations_list)
+    except Exception as e:
+        print(f"Error: {e}")
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == '__main__':
+    app.run(debug=True)

requirements.txt

@@ -1,2 +1,4 @@
 flask
-gunicorn
+gunicorn
+scipy
+pandas