99 lines
3.1 KiB
Python
99 lines
3.1 KiB
Python
import pandas as pd
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import mysql.connector
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import os
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from flask_cors import CORS
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from flask import (
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Blueprint, request, jsonify
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)
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conn = None
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bp = Blueprint('routes', __name__,)
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@bp.route('/chat/request', methods=('GET', 'POST'))
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def handle_request():
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user_input = request.args.get('question')
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top_matches = find_closest_match(user_input)
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recommendations = []
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for similarity, name, ruleId in top_matches:
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finalValue = f"https://communityrule.info/create/?r={ruleId}"
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recommendations.append({
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'community': name,
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'link': finalValue,
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})
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return jsonify(recommendations)
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def get_db_connection():
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global conn
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if conn is None or not conn.is_connected():
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conn = mysql.connector.connect(
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host=os.getenv('CLOUDRON_MYSQL_HOST'),
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user=os.getenv('CLOUDRON_MYSQL_USERNAME'),
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port=os.getenv('CLOUDRON_MYSQL_PORT'),
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password=os.getenv('CLOUDRON_MYSQL_PASSWORD'),
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database=os.getenv('CLOUDRON_MYSQL_DATABASE')
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)
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print("Database connection was successful")
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return conn
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def find_closest_match(user_input):
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conn = get_db_connection()
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max_similarity_curr = -1
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closest_value = None
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rule_id = None
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user_tokens = user_input.split()
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results = []
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cursor = conn.cursor()
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cursor.execute("SELECT summary, modules, name, rule_id FROM rules WHERE deleted = 0")
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rows = cursor.fetchall()
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cursor.close()
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# Compare user input with each value in the compare columns
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for compare_value1, compare_value2, return_value, ruleId in rows:
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# Convert compare_value2 to string if it's a float
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if isinstance(compare_value1, float):
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compare_value1 = str(compare_value1)
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if isinstance(compare_value2, float):
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compare_value2 = str(compare_value2)
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# Split compare_value2 into tokens
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compare_tokens1 = compare_value1.split()
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compare_tokens2 = compare_value2.split()
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# Calculate similarity between user input and compare_value1 and compare_value2
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similarity1 = similarity_score(user_tokens, compare_tokens1)
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similarity2 = similarity_score(user_tokens, compare_tokens2)
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# Take the maximum similarity between similarity1 and similarity2
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max_similarity_curr = max(similarity1, similarity2)
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# Update closest value if current similarity is greater
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#if max_similarity_curr > max_similarity:
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#max_similarity = max_similarity_curr
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#closest_value = return_value
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#rule_id = ruleId
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results.append((max_similarity_curr, return_value, ruleId))
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# print(results)
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results.sort(reverse=True, key=lambda x: x[0])
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return results[:5]
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def similarity_score(set1, set2):
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# Calculate the intersection of the two sets
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intersection = len(set(set1).intersection(set(set2)))
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# Calculate the union of the two sets
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union = len(set(set1).union(set(set2)))
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# Calculate the Jaccard similarity coefficient
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if union == 0:
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return 0
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else:
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return intersection / union
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