Delete app.py

app.py

@@ -1,188 +0,0 @@
-import warnings
-warnings.filterwarnings("ignore")
-
-import os
-import glob
-import textwrap
-import time
-
-import langchain
-
-# Loaders
-from langchain.document_loaders import PyPDFLoader, DirectoryLoader
-
-# Splits
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-
-# Prompts
-from langchain import PromptTemplate, LLMChain
-
-# Vector stores
-from langchain.vectorstores import FAISS
-
-# Import HuggingFacePipeline from the new package
-from langchain_huggingface import HuggingFacePipeline
-from langchain.embeddings import HuggingFaceInstructEmbeddings
-
-# Retrievers
-from langchain.chains import RetrievalQA
-
-import torch
-import transformers
-from transformers import (
-    AutoTokenizer, AutoModelForCausalLM,
-    pipeline
-)
-import gradio as gr
-import locale
-import shutil
-
-# Clear transformers cache
-transformers.logging.set_verbosity_error()
-shutil.rmtree('./.cache', ignore_errors=True)
-
-class CFG:
-    # LLMs configuration
-    model_name = 'llama2-13b-chat'
-    temperature = 0
-    top_p = 0.95
-    repetition_penalty = 1.15
-
-    # Text splitting configuration
-    split_chunk_size = 800
-    split_overlap = 0
-
-    # Embeddings configuration
-    embeddings_model_repo = 'sentence-transformers/all-MiniLM-L6-v2'
-
-    # Similar passages configuration
-    k = 6
-
-    # File paths configuration
-    PDFs_path = './'
-    Embeddings_path = './faiss-hp-sentence-transformers'
-    Output_folder = './rag-vectordb'
-
-def get_model(model=CFG.model_name):
-    print('\nDownloading model: ', model, '\n\n')
-    
-    model_repo = 'daryl149/llama-2-13b-chat-hf' if model == 'llama2-13b-chat' else None
-    
-    if not model_repo:
-        raise ValueError("Model not implemented: " + model)
-
-    tokenizer = AutoTokenizer.from_pretrained(model_repo, use_fast=True)
-    model = AutoModelForCausalLM.from_pretrained(
-        model_repo,
-        device_map="auto",
-        offload_folder="./offload",
-        trust_remote_code=True
-    )
-    
-    max_len = 2048
-    
-    return tokenizer, model, max_len
-
-def wrap_text_preserve_newlines(text, width=700):
-    lines = text.split('\n')
-    wrapped_lines = [textwrap.fill(line, width=width) for line in lines]
-    
-    return '\n'.join(wrapped_lines)
-
-def process_llm_response(llm_response):
-    ans = wrap_text_preserve_newlines(llm_response['result'])
-    
-    sources_used = ' \n'.join(
-        [
-            f"{source.metadata['source'].split('/')[-1][:-4]} - page: {source.metadata['page']}"
-            for source in llm_response['source_documents']
-        ]
-    )
-
-    return ans + '\n\nSources: \n' + sources_used
-
-def llm_ans(query):
-    start = time.time()
-    
-    llm_response = qa_chain.invoke(query)
-    ans = process_llm_response(llm_response)
-    
-    end = time.time()
-    
-    time_elapsed_str = f'\n\nTime elapsed: {int(round(end - start))} s'
-    
-    return ans + time_elapsed_str
-
-def predict(message, history):
-     output = str(llm_ans(message)).replace("\n", "<br/>")
-     return output
-
-tokenizer, model, max_len = get_model(model=CFG.model_name)
-
-pipe = pipeline(
-    task="text-generation",
-    model=model,
-    tokenizer=tokenizer,
-    pad_token_id=tokenizer.eos_token_id,
-    max_length=max_len,
-    temperature=CFG.temperature,
-    top_p=CFG.top_p,
-    repetition_penalty=CFG.repetition_penalty
-)
-
-# Use the updated HuggingFacePipeline class from langchain_huggingface
-llm = HuggingFacePipeline(pipeline=pipe)
-
-loader = DirectoryLoader(
-    CFG.PDFs_path,
-    glob="./*.pdf",
-    loader_cls=PyPDFLoader,
-)
-
-documents = loader.load()
-text_splitter = RecursiveCharacterTextSplitter(
-    chunk_size=CFG.split_chunk_size,
-    chunk_overlap=CFG.split_overlap
-)
-
-texts = text_splitter.split_documents(documents)
-
-vectordb = FAISS.from_documents(
-    texts,
-    HuggingFaceInstructEmbeddings(model_name='sentence-transformers/all-mpnet-base-v2')
-)
-
-# Persist vector database
-vectordb.save_local(f"{CFG.Output_folder}/faiss_index_rag")
-
-retriever = vectordb.as_retriever(search_kwargs={"k": CFG.k})
-
-qa_chain = RetrievalQA.from_chain_type(
-    llm=llm,
-    chain_type="stuff",
-)
-
-prompt_template = """
-Don't try to make up an answer; if you don't know just say that you don't know.
-Answer in the same language the question was asked.
-Use only the following pieces of context to answer the question at the end.
-
-{context}
-
-Question: {question}
-Answer:"""
-
-PROMPT = PromptTemplate(
-   template=prompt_template,
-   input_variables=["context", "question"]
-)
-
-locale.getpreferredencoding = lambda: "UTF-8"
-
-demo = gr.ChatInterface(
-     fn=predict,
-     title=f'Open-Source LLM ({CFG.model_name}) Question Answering'
-)
-
-demo.queue()
-demo.launch()