Final Chatbot code

.gitignore

@@ -0,0 +1,3 @@
+myenv/
+__pycache__/
+

chatbot/__init__.py

@@ -0,0 +1,12 @@
+from flask import Flask
+from flask_cors import CORS
+
+def create_app(test_config=None):
+    # create and configure the app
+    app = Flask(__name__, instance_relative_config=True)
+    CORS(app)
+
+    from . import routes
+    app.register_blueprint(routes.bp)
+
+    return app

chatbot/routes.py

@@ -0,0 +1,103 @@
+import pandas as pd
+from flask_cors import CORS
+from flask import (
+    Blueprint, request, jsonify
+)
+
+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'))
+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'])
+    
+    recommendations = []
+    for similarity, name, ruleId in top_matches:
+        finalValue = f"https://communityrule.info/create/?r={ruleId}"
+        recommendations.append({
+            'community': name,
+            'link': finalValue,
+        })
+
+    print(jsonify(recommendations))
+    return jsonify(recommendations)
+
+
+    #finalValue = "https://communityrule.info/create/?r=" + str(ruleId)
+    #response_message = f"{closest_value}"
+
+    #response_data = {
+    #    'community': response_message,
+    #    'link': finalValue
+    #}
+
+    #return jsonify(response_data)
+
+def find_closest_match(user_input, compare_column1_values, compare_column2_values, return_column_values, rule_id_values):
+    print(user_input)
+    max_similarity_curr = -1
+    closest_value = None
+    rule_id = None
+    user_tokens = user_input.split()
+    results = []
+    # 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):
+        # Convert compare_value2 to string if it's a float
+        if isinstance(compare_value1, float):
+            compare_value1 = str(compare_value1)
+        if isinstance(compare_value2, float):
+            compare_value2 = str(compare_value2)
+
+        # Split compare_value2 into tokens
+        compare_tokens1 = compare_value1.split()
+        compare_tokens2 = compare_value2.split()
+
+        # Calculate similarity between user input and compare_value1 and compare_value2
+        similarity1 = similarity_score(user_tokens, compare_tokens1)
+        similarity2 = similarity_score(user_tokens, compare_tokens2)
+
+        # 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
+            #closest_value = return_value
+            #rule_id = ruleId
+        results.append((max_similarity_curr, return_value, ruleId))
+        # print(results)
+    results.sort(reverse=True, key=lambda x: x[0])
+    return results[:3]
+
+def similarity_score(set1, set2):
+    # Calculate the intersection of the two sets
+    intersection = len(set(set1).intersection(set(set2)))
+    
+    # Calculate the union of the two sets
+    union = len(set(set1).union(set(set2)))
+    
+    # Calculate the Jaccard similarity coefficient
+    if union == 0:
+        return 0
+    else:
+        return intersection / union
+    

requirements.txt

@@ -0,0 +1,14 @@
+blinker==1.7.0
+click==8.1.7
+Flask==3.0.3
+Flask-Cors==4.0.0
+itsdangerous==2.1.2
+Jinja2==3.1.3
+MarkupSafe==2.1.5
+numpy==1.26.4
+pandas==2.2.2
+python-dateutil==2.9.0.post0
+pytz==2024.1
+six==1.16.0
+tzdata==2024.1
+Werkzeug==3.0.2

run.py

@@ -0,0 +1,6 @@
+from chatbot import create_app
+
+app = create_app()
+
+if __name__ == '__main__':
+    app.run()