Initial commit

app.py

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+from flask import Flask, render_template, request, jsonify
+import re
+import nltk
+from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+
+#Initialize the Flask application
+app = Flask(__name__)
+
+#Define a route for the index page
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+#Define a route for the chatbot response API:
+
+@app.route('/chat/getResponse', methods=['GET'])
+def get_response():
+    user_message = request.args.get('userMessage')
+    response = chatbot_answer(user_message)
+    return jsonify(response)
+
+
+# Function to preprocess the text
+def preprocess_text(text):
+    # Convert to lowercase
+    text = text.lower()
+    # Replace numbers with spaces
+    text = re.sub(r'\[[0-9]*\]', ' ', text)
+    # Replace multiple whitespaces with a single whitespace
+    text = re.sub(r'\s+', ' ', text)
+    return text
+
+# Open the mean_reversion txt web data 
+with open('./mean_reversion.txt', 'r') as file:
+    mrev_data = file.read()
+
+# Find all the sentences in the mean_reversion txt
+mrev_text = preprocess_text(mrev_data)
+mrev_sentences = nltk.sent_tokenize(mrev_text)
+
+def chatbot_answer(user_query):
+    # Append the query to the sentences list
+    mrev_sentences.append(user_query)
+    # Create the sentences vector based on the list
+    vectorizer = TfidfVectorizer()
+    sentences_vectors = vectorizer.fit_transform(mrev_sentences)
+    # Measure the cosine similarity and take the second closest index because the first index is the user query
+    vector_values = cosine_similarity(sentences_vectors[-1], sentences_vectors)
+    answer = mrev_sentences[vector_values.argsort()[0][-2]]
+    # Final check to make sure there are results present. If all the results are 0, it means the text input by us is not captured in the corpus
+    input_check = vector_values.flatten()
+    input_check.sort()
+    if input_check[-2] == 0:
+        return "Please try again"
+    else:
+        return answer
+
+
+#print("Hello, I am the MoonAlphas Chatbot. What are your questions?")
+#while True:
+ #   query = input().lower()
+  #  if query not in ['bye', 'good bye', 'take care']:
+   #     print("MoonAlphas Chatbot: ", end="")
+    #    print(chatbot_answer(query))
+     #   mrev_sentences.remove(query)
+    #else:
+     #   print("See you again")
+      #  break
+
+
+if __name__ == '__main__':
+    app.run()