Create app.py

app.py

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+import nltk
+from nltk.corpus import stopwords
+from nltk.stem import PorterStemmer
+from nltk.tokenize import word_tokenize
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+from flask import Flask, request, jsonify
+
+# Initialize the Flask app
+app = Flask(__name__)
+
+# Download necessary NLTK data
+nltk.download('punkt')
+nltk.download('stopwords')
+
+# Load customer inquiries dataset
+with open('my_text_file.txt', 'r') as f:
+    data = f.readlines()
+
+# Preprocess data
+def preprocess_text(text):
+    tokens = word_tokenize(text.lower())
+    stop_words = set(stopwords.words('english'))
+    filtered_tokens = [word for word in tokens if word not in stop_words]
+    stemmer = PorterStemmer()
+    stemmed_tokens = [stemmer.stem(word) for word in filtered_tokens]
+    return stemmed_tokens
+
+# Create TF-IDF vectorizer
+vectorizer = TfidfVectorizer(analyzer=preprocess_text)
+tfidf_matrix = vectorizer.fit_transform(data)
+
+# Define chatbot logic
+def chatbot_response(user_input):
+    preprocessed_input = preprocess_text(user_input)
+    input_vector = vectorizer.transform([user_input])
+    cosine_similarities = cosine_similarity(input_vector, tfidf_matrix)
+    most_similar_index = cosine_similarities.argmax()
+    return data[most_similar_index].strip()
+
+# Define routes
+@app.route('/')
+def home():
+    return "Welcome to the Chatbot! Send a POST request to /chat with your message."
+
+@app.route('/chat', methods=['POST'])
+def chat():
+    user_input = request.json.get('message')
+    if user_input:
+        response = chatbot_response(user_input)
+        return jsonify({'response': response})
+    else:
+        return jsonify({'error': 'No message provided'}), 400
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=8080)