app.py

import pickle
import gradio as gr
from chatbot import answer_query_with_context
from file_utils import load_service_data, load_pickle

database_filepath = 'services-links.csv'
embeddings_filepath = 'document_embeddings.pkl'

database = load_service_data(database_filepath)
database_embeddings = load_pickle(embeddings_filepath)

def chatbot(input):
    try:
        if input:
            reply = answer_query_with_context(input, database, database_embeddings)
            return reply
    except Exception as e:
        return str(e)

# Create a Gradio interface
inputs = gr.Textbox(lines=7, label="Chat with AI")
outputs = gr.Textbox(label="Reply")
header_message = "Ask anything about the following services: "+", ".join(database.index)
iface = gr.Interface(fn=chatbot, 
                     inputs=inputs, 
                     outputs=outputs, 
                     title="AI Chatbot",
                     description=header_message)

if __name__ == "__main__":
    iface.launch()