Final Code

chatbot/routes.py

@@ -1,34 +1,19 @@
 import pandas as pd
+import mysql.connector
+import os
 from flask_cors import CORS
 from flask import (
     Blueprint, request, jsonify
 )
+conn = None
 
 bp = Blueprint('routes', __name__,)
-df = pd.read_csv('rule.csv')
-user_input = "Improve the living and working conditions of everyone everywhere Fair and humane laws and practices"
-compare_column1 = df['summary']
-compare_column2 = df['modules']
-return_column = df['name']
-@bp.route('/request', methods=('GET', 'POST'))
+
+@bp.route('/chat/request', methods=('GET', 'POST'))
 def handle_request():
 
-    #user_input = request.args.get('question')
-    #closest_value, ruleId = find_closest_match(user_input, df['summary'], df['modules'], df['name'], df['ruleID'])
-    
-
-    #finalValue = "https://communityrule.info/create/?r=" + str(ruleId)
-    #response_message = f"{closest_value}"
-
-    #response_data = {
-    #    'community': response_message,
-    #    'link': finalValue
-    #}
-
-    #return jsonify(response_data)
-    
     user_input = request.args.get('question')
-    top_matches = find_closest_match(user_input, df['summary'], df['modules'], df['name'], df['ruleID'])
+    top_matches = find_closest_match(user_input)
     
     recommendations = []
     for similarity, name, ruleId in top_matches:
@@ -38,29 +23,38 @@ def handle_request():
             'link': finalValue,
         })
 
-    print(jsonify(recommendations))
     return jsonify(recommendations)
 
+def get_db_connection():
+    global conn
+    if conn is None or not conn.is_connected():
+        conn = mysql.connector.connect(
+            host=os.getenv('CLOUDRON_MYSQL_HOST'),
+            user=os.getenv('CLOUDRON_MYSQL_USERNAME'),
+            port=os.getenv('CLOUDRON_MYSQL_PORT'),
+            password=os.getenv('CLOUDRON_MYSQL_PASSWORD'),
+            database=os.getenv('CLOUDRON_MYSQL_DATABASE')
+        )
+        print("Database connection was successful")
 
-    #finalValue = "https://communityrule.info/create/?r=" + str(ruleId)
-    #response_message = f"{closest_value}"
+    return conn
 
-    #response_data = {
-    #    'community': response_message,
-    #    'link': finalValue
-    #}
 
-    #return jsonify(response_data)
+def find_closest_match(user_input):
 
-def find_closest_match(user_input, compare_column1_values, compare_column2_values, return_column_values, rule_id_values):
-    print(user_input)
+    conn = get_db_connection()
     max_similarity_curr = -1
     closest_value = None
     rule_id = None
     user_tokens = user_input.split()
     results = []
+    cursor = conn.cursor()
+    cursor.execute("SELECT summary, modules, name, rule_id FROM rules")
+    rows = cursor.fetchall()
+    cursor.close()
+
     # Compare user input with each value in the compare columns
-    for compare_value1, compare_value2, return_value, ruleId in zip(compare_column1_values, compare_column2_values, return_column_values, rule_id_values):
+    for compare_value1, compare_value2, return_value, ruleId in rows:
         # Convert compare_value2 to string if it's a float
         if isinstance(compare_value1, float):
             compare_value1 = str(compare_value1)
@@ -77,7 +71,7 @@ def find_closest_match(user_input, compare_column1_values, compare_column2_value
 
         # Take the maximum similarity between similarity1 and similarity2
         max_similarity_curr = max(similarity1, similarity2)
-        print(similarity1, similarity2)
+
         # Update closest value if current similarity is greater
         #if max_similarity_curr > max_similarity:
             #max_similarity = max_similarity_curr
@@ -100,4 +94,5 @@ def similarity_score(set1, set2):
         return 0
     else:
         return intersection / union
-    
+
+