app.py

import pickle
import gradio as gr
from utils.chatbot import answer_query_with_context
from utils.file_utils import load_database, load_embeddings, load_file

database_filepath = 'data/services.csv'
embeddings_filepath = 'data/document_embeddings.pkl'

database = load_database(database_filepath)
database_embeddings = load_embeddings(database, database_filepath, 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 = load_file('prompts/chabot_header_message.txt')
iface = gr.Interface(fn=chatbot, 
                     inputs=inputs, 
                     outputs=outputs, 
                     title="AI Chatbot",
                     description=header_message)

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