| import gradio as gr |
| import os |
| import logging |
|
|
| from langchain.embeddings.openai import OpenAIEmbeddings |
| from langchain.vectorstores import Chroma |
| from langchain.text_splitter import CharacterTextSplitter |
| from langchain.llms import OpenAI |
| from langchain.chains import ConversationalRetrievalChain |
| from langchain.document_loaders import DirectoryLoader |
|
|
| logging.basicConfig(filename='./Logs/bot.log', level=logging.WARNING, format='%(asctime)s - %(levelname)s - %(message)s') |
|
|
|
|
| txt_loader = DirectoryLoader('./LVE/', glob="**/*.txt") |
| pdf_loader = DirectoryLoader('./LVE/', glob="**/*.pdf") |
| doc_loader = DirectoryLoader('./LVE/', glob="**/*.docx") |
| loaders = [pdf_loader, txt_loader, doc_loader] |
| documents = [] |
|
|
| for loader in loaders: |
| documents.extend(loader.load()) |
| |
| print(f"Total # of documents: {len(documents)}") |
|
|
| text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0) |
| documents = text_splitter.split_documents(documents) |
|
|
| embeddings = OpenAIEmbeddings() |
| vectorstore = Chroma.from_documents(documents, embeddings) |
|
|
| from langchain.memory import ConversationBufferMemory |
| |
|
|
| qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever()) |
|
|
| |
|
|
| def submit_callback(user_message): |
| default_prompt = "For answers, refer to the provided content. If no answer is found, contact lveswim@gmail.com." |
| prompt = default_prompt + user_message |
| |
| logging.info(f"User Query: {user_message}") |
|
|
| input_data = {"question": prompt, "chat_history": []} |
| response = qa(input_data) |
|
|
| |
| logging.info(f"Chatbot Response: {response['answer']}") |
| return response["answer"] |
|
|
|
|
| iface = gr.Interface( |
| fn=submit_callback, |
| inputs=gr.inputs.Textbox(lines=2, label="Enter your query"), |
| outputs=gr.outputs.Textbox(label="Chatbot Response"), |
| |
| title="LVE Torpedoes Chatbot", |
| layout="vertical", |
| description="Enter your query to chat with the LVET chatbot", |
| examples=[ |
| ["What are the practice times for each age group ?"], |
| ["What are the required fields to set up a meet in Touchpad?"], |
| ["Dryland workout for swimmers ?"], |
| ["What are the eligibility criteria for the Mini Torpedoes program?"], |
| ["What is the eligibility to participate in the LVET Swim Team?"], |
| ["How many volunteer hours are required per family during the swim season?"], |
| ["How can I receive credit hours for the official training?"], |
| ["How are swimmers grouped for practice?"], |
| ["When do evaluations take place for new swimmers?"], |
| ["Who are LVET's Board Members"], |
| ["What are the regular season meets start times?"], |
| ["How can I contact LVET's Board Members?"], |
| ["What is the penalty for not meeting the required volunteer hours?"], |
| ["Volunteer Hours?"], |
| ["What types of events can a swimmer enter and how many?"], |
| ["How do I sign up for volunteer jobs to fulfill my volunteer hours?"], |
| ["Volunteer jobs that do not require certification or prior experience"], |
| ["What are the responsibilities of an Age Group Coordinator?"], |
| ["How do I commit my swimmer for meets/events?"], |
| ["How are timers distributed between the host and visiting teams in dual meets?"], |
| ["What happens if a watch malfunctions during an event?"], |
| ["What is the difference between the Divisional Meets and the All Star Meet?"], |
| ["What is the ODSL Scholarship Program and what's the award ?"] |
| |
| ], |
| theme="default" |
| |
| ) |
| iface.launch() |
