app.py

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
from llama_index.llms.openai import OpenAI
from datasets import load_dataset
import gradio as gr
import os

#Configure sua API da OpenAI (ou pode trocar por HuggingFace LLM depois)
os.environ["OPENAI_API_KEY"] = "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"  # Coloque sua chave da OpenAI aqui

#Carregar dataset do Hugging Face
dataset = load_dataset("Jeice/n8n-docs", split="train")

#Salvar os arquivos localmente
os.makedirs("docs", exist_ok=True)

for item in dataset:
    if "file" in item and item["file"]["path"].endswith(('.md', '.txt')):
        file_name = item["file"]["path"].split("/")[-1]
        with open(f"docs/{file_name}", "w", encoding="utf-8") as f:
            f.write(item["text"])

#Criar o index
documents = SimpleDirectoryReader('docs').load_data()

service_context = ServiceContext.from_defaults(
    llm=OpenAI(model="gpt-3.5-turbo")
)

index = VectorStoreIndex.from_documents(documents, service_context=service_context)
query_engine = index.as_query_engine()

#Função do chatbot
def chatbot(input_text):
    response = query_engine.query(input_text)
    return str(response)

#Interface Gradio
interface = gr.Interface(
    fn=chatbot,
    inputs=gr.Textbox(lines=2, placeholder="Digite sua pergunta sobre o n8n aqui..."),
    outputs="text",
    title="🤖 Bot de Dúvidas sobre o n8n",
    description="Consulte a documentação oficial do n8n. Pergunte sobre workflows, nodes e integrações!",
    theme="default",
    examples=[
        ["Como criar um workflow no n8n?"],
        ["Para que serve o node HTTP Request?"],
        ["Quais são os nodes de integração com Google Sheets?"],
    ],
    allow_flagging="never"
)

interface.launch()