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| import os | |
| from threading import Lock | |
| from typing import Any, Dict, Optional, Tuple | |
| import gradio as gr | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.prompts.chat import (ChatPromptTemplate, | |
| HumanMessagePromptTemplate, | |
| SystemMessagePromptTemplate) | |
| from src.core.chunking import chunk_file | |
| from src.core.embedding import embed_files | |
| from src.core.parsing import read_file | |
| VECTOR_STORE = "faiss" | |
| MODEL = "openai" | |
| EMBEDDING = "openai" | |
| MODEL = "gpt-3.5-turbo-16k" | |
| K = 5 | |
| USE_VERBOSE = True | |
| API_KEY = os.environ["OPENAI_API_KEY"] | |
| system_template = """ | |
| The context below contains excerpts from 'Ben Hogan's Five Lessions'. You must only use the information in the context below to formulate your response. If there is not enough information to formulate a response, you must respond with | |
| "I'm sorry, but I can't find the answer to your question in, Ben Hogan's Five Lessons." | |
| Here is the context: | |
| {context} | |
| {chat_history} | |
| """ | |
| # Create the chat prompt templates | |
| messages = [ | |
| SystemMessagePromptTemplate.from_template(system_template), | |
| HumanMessagePromptTemplate.from_template("{question}") | |
| ] | |
| qa_prompt = ChatPromptTemplate.from_messages(messages) | |
| class AnswerConversationBufferMemory(ConversationBufferMemory): | |
| def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: | |
| return super(AnswerConversationBufferMemory, self).save_context(inputs,{'response': outputs['answer']}) | |
| def getretriever(): | |
| with open("./resources/Ben_Hogans_Five_Lessons.pdf", 'rb') as uploaded_file: | |
| try: | |
| file = read_file(uploaded_file) | |
| except Exception as e: | |
| print(e) | |
| chunked_file = chunk_file(file, chunk_size=512, chunk_overlap=0) | |
| folder_index = embed_files( | |
| files=[chunked_file], | |
| embedding=EMBEDDING, | |
| vector_store=VECTOR_STORE, | |
| openai_api_key=API_KEY, | |
| ) | |
| return folder_index.index.as_retriever(verbose=True, search_type="similarity", search_kwargs={"k": K}) | |
| retriever = getretriever() | |
| def getanswer(chain, question, history): | |
| if hasattr(chain, "value"): | |
| chain = chain.value | |
| if hasattr(history, "value"): | |
| history = history.value | |
| if hasattr(question, "value"): | |
| question = question.value | |
| history = history or [] | |
| lock = Lock() | |
| lock.acquire() | |
| try: | |
| output = chain({"question": question}) | |
| output = output["answer"] | |
| history.append((question, output)) | |
| except Exception as e: | |
| raise e | |
| finally: | |
| lock.release() | |
| return history, history, gr.update(value="") | |
| def load_chain(inputs = None): | |
| llm = ChatOpenAI( | |
| openai_api_key=API_KEY, | |
| model_name=MODEL, | |
| verbose=True) | |
| chain = ConversationalRetrievalChain.from_llm( | |
| llm, | |
| retriever=retriever, | |
| return_source_documents=USE_VERBOSE, | |
| memory=AnswerConversationBufferMemory(memory_key="chat_history", return_messages=True), | |
| verbose=USE_VERBOSE, | |
| combine_docs_chain_kwargs={"prompt": qa_prompt}) | |
| return chain | |
| with gr.Blocks() as block: | |
| with gr.Row(): | |
| with gr.Column(scale=0.75): | |
| with gr.Row(): | |
| gr.Markdown("<h1>Ben Hogan's Five Lessons</h1>") | |
| with gr.Row(): | |
| gr.Markdown("by Ben Hogan") | |
| chatbot = gr.Chatbot(elem_id="chatbot").style(height=600) | |
| with gr.Row(): | |
| message = gr.Textbox( | |
| label="", | |
| placeholder="Ask Ben...", | |
| lines=1, | |
| ) | |
| with gr.Row(): | |
| submit = gr.Button(value="Send", variant="primary", scale=1) | |
| state = gr.State() | |
| chain_state = gr.State(load_chain) | |
| submit.click(getanswer, inputs=[chain_state, message, state], outputs=[chatbot, state, message]) | |
| message.submit(getanswer, inputs=[chain_state, message, state], outputs=[chatbot, state, message]) | |
| with gr.Column(scale=0.25): | |
| with gr.Row(): | |
| gr.Markdown("<h1><center>Suggestions</center></h1>") | |
| ex1 = gr.Button(value="What is this book about?", variant="primary") | |
| ex1.click(getanswer, inputs=[chain_state, ex1, state], outputs=[chatbot, state, message]) | |
| ex2 = gr.Button(value="What are the core fundamentals Ben teaches?", variant="primary") | |
| ex2.click(getanswer, inputs=[chain_state, ex2, state], outputs=[chatbot, state, message]) | |
| ex3 = gr.Button(value="How can I improve my swing?", variant="primary") | |
| ex3.click(getanswer, inputs=[chain_state, ex3, state], outputs=[chatbot, state, message]) | |
| block.launch(debug=True) |