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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 = """ | |
| You are a helpful assistant responding to inqueries about the content of the book Freakonomics, by Steven D. Levitt and Stephen J. Dubner. The context below contains excerpts from the book. You must only use the information in the context below to formulate your responses. If there is not enough information in the context to formulate a response, you must respond with | |
| "I'm sorry, but I can't find the answer to your question in, the book Freakonomics." | |
| 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/Freakonomics.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}) | |
| chain.rephrase_question = False | |
| return chain | |
| with gr.Blocks() as block: | |
| with gr.Row(): | |
| with gr.Column(scale=0.75): | |
| with gr.Row(): | |
| gr.Markdown("<h1>Freakonomics</h1>") | |
| with gr.Row(): | |
| gr.Markdown("by Steven D. Levitt and Stephen J. Dubner") | |
| chatbot = gr.Chatbot(elem_id="chatbot").style(height=600) | |
| with gr.Row(): | |
| message = gr.Textbox( | |
| label="", | |
| placeholder="Freakonomics", | |
| 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="How does the book challenge conventional wisdom?", variant="primary") | |
| ex1.click(getanswer, inputs=[chain_state, ex1, state], outputs=[chatbot, state, message]) | |
| ex2 = gr.Button(value="What are some of the surprising and counterintuitive examples from the book?", variant="primary") | |
| ex2.click(getanswer, inputs=[chain_state, ex2, state], outputs=[chatbot, state, message]) | |
| ex3 = gr.Button(value="How does the book explore the role of incentives?", variant="primary") | |
| ex3.click(getanswer, inputs=[chain_state, ex3, state], outputs=[chatbot, state, message]) | |
| block.launch(debug=True) |