Duplicate from JUNGU/talktosayno

Co-authored-by: HAN JUNGU <JUNGU@users.noreply.huggingface.co>

.gitattributes

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+docs.pdf filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+title: Talktosayno
+emoji: 📉
+colorFrom: green
+colorTo: pink
+sdk: gradio
+sdk_version: 3.34.0
+app_file: app.py
+pinned: false
+license: openrail
+duplicated_from: JUNGU/talktosayno
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from langchain.chat_models import ChatOpenAI
+from langchain.document_loaders import PyPDFLoader
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.embeddings.cohere import CohereEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch
+from langchain.vectorstores import Chroma
+from PyPDF2 import PdfWriter
+import gradio as gr
+import os
+from dotenv import load_dotenv
+import openai
+
+load_dotenv()
+#비밀키 가져오기 시도중
+# api_key = os.getenv('OPENAI_API_KEY') ## .env 파일 업로드하면 숨겨지지 않음 안됨
+# api_key = os.environ['my_secret'] ## 안불러와짐
+# api_key = os.getenv('my_secret') ## 3트 .env 대신 secret키를 불러오는 형태로 도전
+os.environ["OPENAI_API_KEY"] = os.environ['my_secret']
+
+loader = PyPDFLoader("/home/user/app/docs.pdf")
+documents = loader.load()
+
+text_splitter = CharacterTextSplitter(chunk_size=800, chunk_overlap=0)
+texts = text_splitter.split_documents(documents)
+
+#vector embedding
+embeddings = OpenAIEmbeddings()
+vector_store = Chroma.from_documents(texts, embeddings)
+retriever = vector_store.as_retriever(search_kwargs={"k": 2})
+
+from langchain.chat_models import ChatOpenAI
+from langchain.chains import RetrievalQAWithSourcesChain
+
+llm = ChatOpenAI(model_name="gpt-4", temperature=0)  # Modify model_name if you have access to GPT-4
+
+chain = RetrievalQAWithSourcesChain.from_chain_type(
+    llm=llm,
+    chain_type="stuff",
+    retriever = retriever,
+    return_source_documents=True)
+
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+
+system_template="""Use the following pieces of context to answer the users question shortly.
+Given the following summaries of a long document and a question, create a final answer with references ("SOURCES"), use "SOURCES" in capital letters regardless of the number of sources.
+If you don't know the answer, just say that "I don't know", don't try to make up an answer.
+----------------
+{summaries}
+
+You MUST answer in Korean and in Markdown format:"""
+
+messages = [
+    SystemMessagePromptTemplate.from_template(system_template),
+    HumanMessagePromptTemplate.from_template("{question}")
+]
+
+prompt = ChatPromptTemplate.from_messages(messages)
+
+from langchain.chat_models import ChatOpenAI
+from langchain.chains import RetrievalQAWithSourcesChain
+
+chain_type_kwargs = {"prompt": prompt}
+
+llm = ChatOpenAI(model_name="gpt-4", temperature=0)  # Modify model_name if you have access to GPT-4
+
+chain = RetrievalQAWithSourcesChain.from_chain_type(
+    llm=llm,
+    chain_type="stuff",
+    retriever = retriever,
+    return_source_documents=True,
+    chain_type_kwargs=chain_type_kwargs
+)
+
+query = "행복한 인생이란?"
+result = chain(query)
+
+
+for doc in result['source_documents']:
+    print('내용 : ' + doc.page_content[0:100].replace('\n', ' '))
+    print('파일 : ' + doc.metadata['source'])
+    print('페이지 : ' + str(doc.metadata['page']))
+
+
+def respond(message, chat_history):  # 채팅봇의 응답을 처리하는 함수를 정의합니다.
+
+    result = chain(message)
+
+    bot_message = result['answer']
+
+    for i, doc in enumerate(result['source_documents']):
+        bot_message += '[' + str(i+1) + '] ' + doc.metadata['source'] + '(' + str(doc.metadata['page']) + ') '
+
+    chat_history.append((message, bot_message))  # 채팅 기록에 사용자의 메시지와 봇의 응답을 추가합니다.
+
+    return "", chat_history  # 수정된 채팅 기록을 반환합니다.
+
+with gr.Blocks(theme='gstaff/sketch') as demo:  # gr.Blocks()를 사용하여 인터페이스를 생성합니다.
+    gr.Markdown("# 안녕하세요. 세이노와 대화해보세요.")
+    chatbot = gr.Chatbot(label="채팅창")  # '채팅창'이라는 레이블을 가진 채팅봇 컴포넌트를 생성합니다.
+    msg = gr.Textbox(label="입력")  # '입력'이라는 레이블을 가진 텍스트박스를 생성합니다.
+    clear = gr.Button("초기화")  # '초기화'라는 레이블을 가진 버튼을 생성합니다.
+
+    msg.submit(respond, [msg, chatbot], [msg, chatbot])  # 텍스트박스에 메시지를 입력하고 제출하면 respond 함수가 호출되도록 합니다.
+    clear.click(lambda: None, None, chatbot, queue=False)  # '초기화' 버튼을 클릭하면 채팅 기록을 초기화합니다.
+demo.launch(debug=True)  # 인터페이스를 실행합니다. 실행하면 사용자는 '입력' 텍스트박스에 메시지를 작성하고 제출할 수 있으며, '초기화' 버튼을 통해 채팅 기록을 초기화 할 수 있습니다.

docs.pdf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dab840d01bd8582e930da5ccb74c032279e832ed02f7f938953e7f77730d1ad2
+size 4232031

requirements.txt

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+openai
+langchain
+pypdf
+chromadb
+tiktoken
+PyPDF2