Delete app.py

app.py

@@ -1,140 +0,0 @@
-import os
-from typing import List, Tuple
-from langchain_community.llms import GPT4All
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.runnables import RunnablePassthrough
-import bs4
-import textwrap
-from langchain.chains import create_retrieval_chain
-#from langchain.chains.combine_documents import create_stuff_documents_chain
-from langchain_community.vectorstores import FAISS
-#from langchain_community.document_loaders import WebBaseLoader
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_community.embeddings import LlamaCppEmbeddings
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-
-from langchain_core.callbacks import BaseCallbackHandler
-from langchain_community.document_loaders import TextLoader
-
-from google.colab import drive
-drive.mount('/content/drive')
-
-local_path = "/content/drive/MyDrive/Model/aya-23-8B-Q3_K_S.gguf" # "/content/drive/MyDrive/Dorna-Llama3-8B-Instruct.Q5_0.gguf" # 
-model_path = "/content/drive/MyDrive/Model/labse.Q3_K_S.gguf" # "/content/drive/MyDrive/labse.Q6_K.gguf" # 
-text_path = "/content/drive/MyDrive/gpt4all/docs/Books/chmn.txt"
-index_path = "/content/drive/MyDrive/gpt4all/index_CHEHEL_MAJLESE_NOOR"
-
-def initialize_embeddings() -> LlamaCppEmbeddings:
-    return LlamaCppEmbeddings(model_path=model_path)
-
-def load_documents() -> List:
-    loader = TextLoader(text_path)
-    return loader.load()
-
-def split_chunks(sources: List) -> List:
-    chunks = []
-    splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=32)
-    for chunk in splitter.split_documents(sources):
-        chunks.append(chunk)
-    return chunks
-
-def generate_index(chunks: List, embeddings: LlamaCppEmbeddings) -> FAISS:
-    texts = [doc.page_content for doc in chunks]
-    metadatas = [doc.metadata for doc in chunks]
-    return FAISS.from_texts(texts, embeddings, metadatas=metadatas)
-
-class MyCustomHandler(BaseCallbackHandler):
-    def on_llm_new_token(self, token: str, **kwargs) -> None:             
-        print(token),
-
-llm = GPT4All( model=local_path, n_threads=150, streaming=True,verbose=False)#,device='cuda:Tesla T4') #
-# callbacks=[MyCustomHandler()],
-# # 1. Load, chunk and index the contents of the blog to create a retriever.
-# loader = WebBaseLoader(
-#     web_paths=("https://lilianweng.github.io/posts/2023-06-23-agent/",),
-#     bs_kwargs=dict(
-#         parse_only=bs4.SoupStrainer(
-#             class_=("post-content", "post-title", "post-header")
-#         )
-#     ),
-# )
-def format_docs(docs):
-    return "\n\n".join(doc.page_content for doc in docs)
-
-#docs = loader.load()
-
-#text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-#splits = text_splitter.split_documents(docs)
-#vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())
-#retriever = vectorstore.as_retriever() ########## attention
-
-embeddings = initialize_embeddings()
-rebuilIndex = input('Rebuild Index (y/n)?')
-if rebuilIndex=='y':
-  #start = time.time()
-  sources = load_documents()
-  chunks = split_chunks(sources)
-  vectorstore = generate_index(chunks, embeddings)
-  vectorstore.save_local(index_path)
-  #end = time.time()
-  #elapsed = end - start
-  #print('Elapsed time to build index: ' + str(elapsed))
-
-index = FAISS.load_local(index_path, embeddings,allow_dangerous_deserialization=True)
-
-retriver = index.as_retriever()
-
-# 2. Incorporate the retriever into a question-answering chain.
-system_prompt = (    
-    """You are an assistant for question-answering tasks. "
-    "Only use the {context} to answer: "   
-    "لطفاً فقط به زبان فارسی صحبت کن و تمام پاسخ ها را به زبان فارسی بنویس "    
-    "لطفا پاسخ هایت طولانی باشد "   
-     "اگر پاسخ سوال را نیافتی بگو نمیدانم"
-    "\n\n"""
-    
-)
-
-prompt = ChatPromptTemplate.from_messages(
-    [
-        ("system", system_prompt),
-        ("human", "{input}"),
-    ]
-)
-
-##question_answer_chain = create_stuff_documents_chain(llm, prompt)
-##rag_chain = create_retrieval_chain(retriver  , question_answer_chain) # retriever
-
-#result = rag_chain.invoke({"input": "What is Task Decomposition?"})
-
-# second edit
-
-rag_chain_from_docs = (
-    {
-        "input": lambda x: x["input"],  # input query
-        "context": lambda x: format_docs(x["context"]),  # context
-    }
-    | prompt  # format query and context into prompt
-    | llm  # generate response
-    | StrOutputParser()  # coerce to string
-)
-
-# Pass input query to retriever
-retrieve_docs = (lambda x: x["input"]) | retriver
-
-# Below, we chain `.assign` calls. This takes a dict and successively
-# adds keys-- "context" and "answer"-- where the value for each key
-# is determined by a Runnable. The Runnable operates on all existing
-# keys in the dict.
-chain = RunnablePassthrough.assign(context=retrieve_docs).assign(
-    answer=rag_chain_from_docs
-)
-chat_history = []
-while True:
-    query = input("پرسش تان را بپرسید. حقیر در خدمتم: ")
-    
-    if query.lower() == 'exit':
-        break
-
-    response = chain.invoke({"input": query})
-    print(textwrap.fill(response['answer'],80))