| import gradio as gr | |
| import os | |
| from langchain_openai import OpenAIEmbeddings | |
| from langchain_community.document_loaders import TextLoader | |
| from langchain_openai import ChatOpenAI | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain_community.vectorstores import FAISS | |
| from langchain.chains import RetrievalQA | |
| from langchain.chains import ConversationalRetrievalChain | |
| OpenAIModel = "gpt-3.5-turbo" | |
| OPENAI_API_KEY = os.environ["OPENAI_API_KEY"] | |
| llm = ChatOpenAI(model=OpenAIModel, temperature=0.1, openai_api_key=OPENAI_API_KEY) | |
| def ask(text): | |
| answer = qa.run(text) | |
| return answer | |
| loader = TextLoader("test.txt") | |
| data = loader.load() | |
| embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY) | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=50) | |
| all_splits = text_splitter.split_documents(data) | |
| db2 = FAISS.from_documents(all_splits, embeddings) | |
| qa = RetrievalQA.from_chain_type(llm=llm, retriever=db2.as_retriever()) | |
| iface = gr.Interface(ask,gr.Textbox(label="Question"),gr.Textbox(label="Answer"), title="BiMah Customer Service Chatbot",description="A chatbot that can answer things related to BiMah (Bimbel Mahasiswa)", examples=["How BiMah can enforce students to be better?","Siapa CEO BiMah?", "Bagaimana langkah-langkah pendaftaran di BiMah?"]) | |
| iface.launch() | |