import gradio as gr
from llama_index.readers.file import PagedCSVReader
from llama_index.core.indices import VectorStoreIndex
import openai
import os

openai.api_key = os.getenv('OPENAI_API_KEY')

def load_data():
    try:
        loader = PagedCSVReader()
        documents = loader.load_data('/content/aitalents.csv')
        index = VectorStoreIndex.from_documents(documents)
        query_engine = index.as_query_engine()
        return query_engine
    except Exception as e:
        print(f"Error loading data or creating index: {e}")
        return None

query_engine = load_data()  # Call load_data() to create the query engine

def chat(message):
    if query_engine is None:
        return "An error occurred while loading data. Please try again later."

    try:
        response = query_engine.query(message)
        return response[0]['text']
    except Exception as e:
        print(f"Error generating response: {e}")
        return "I'm still learning how to answer that question. Please try asking something else."

# Create the chatbot interface
interface = gr.ChatInterface(fn=chat, title="Chatbot with Llama Hub and OpenAI", initial_refresh=False)

# Launch the interface
interface.launch()