Update app.py

app.py

@@ -4,19 +4,14 @@ 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
-import os
-
-# Initialize the Flask app
-app = Flask(__name__)
+import gradio as gr
 
 # Download necessary NLTK data
 nltk.download('punkt')
 nltk.download('stopwords')
 
 # Load customer inquiries dataset
-file_path = os.path.join(os.path.dirname(__file__), 'my_text_file.txt')
-with open(file_path, 'r') as f:
+with open('my_text_file.txt', 'r') as f:
     data = f.readlines()
 
 # Preprocess data
@@ -34,26 +29,21 @@ 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
-
-# Main entry
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=8080)
+# Create Gradio interface
+def chatbot_interface(user_input):
+    response = chatbot_response(user_input)
+    return response
+
+iface = gr.Interface(fn=chatbot_interface, 
+                     inputs="text", 
+                     outputs="text",
+                     title="FAQ Chatbot",
+                     description="Enter a question to get a response from the chatbot based on the preloaded FAQ data.")
+
+if __name__ == "__main__":
+    iface.launch()