Delete app.py

app.py

@@ -1,376 +0,0 @@
-import os
-import glob
-import textwrap
-import time
-import langchain
-import locale
-import gradio as gr
-
-locale.getpreferredencoding = lambda: "UTF-8"
-
-from langchain.document_loaders import PyPDFLoader, DirectoryLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain import PromptTemplate, LLMChain
-
-from langchain.embeddings.huggingface import HuggingFaceEmbeddings
-
-### vector stores
-from langchain.vectorstores import FAISS
-
-### models
-from langchain.llms import HuggingFacePipeline
-from langchain.embeddings import HuggingFaceInstructEmbeddings
-
-### retrievers
-from langchain.chains import RetrievalQA
-
-import torch
-import transformers
-from transformers import (
-    AutoTokenizer, AutoModelForCausalLM,
-    BitsAndBytesConfig,
-    pipeline
-)
-
-sorted(glob.glob('/content/anatomy_vol_*'))
-
-
-def wrap_text_preserve_newlines(text, width=700):
-    # Split the input text into lines based on newline characters
-    lines = text.split('\n')
-
-    # Wrap each line individually
-    wrapped_lines = [textwrap.fill(line, width=width) for line in lines]
-
-    # Join the wrapped lines back together using newline characters
-    wrapped_text = '\n'.join(wrapped_lines)
-
-    return wrapped_text
-
-
-def process_llm_response(llm_response):
-    ans = wrap_text_preserve_newlines(llm_response['result'])
-
-    sources_used = ' \n'.join(
-        [
-            source.metadata['source'].split('/')[-1][:-4]
-            + ' - page: '
-            + str(source.metadata['page'])
-            for source in llm_response['source_documents']
-        ]
-    )
-
-    ans = ans + '\n\nSources: \n' + sources_used
-    return ans
-
-def llm_ans(query):
-    start = time.time()
-
-    llm_response = qa_chain.invoke(query)
-    ans = process_llm_response(llm_response)
-
-    end = time.time()
-
-    time_elapsed = int(round(end - start, 0))
-    time_elapsed_str = f'\n\nTime elapsed: {time_elapsed} s'
-    return ans + time_elapsed_str
-
-
-def predict(message, history):
-    output = str(llm_ans(message)).replace("\n", "<br/>")
-    return output
-
-
-def get_model(model = CFG.model_name):
-
-    if model == 'wizardlm':
-        model_repo = 'TheBloke/wizardLM-7B-HF'
-
-        tokenizer = AutoTokenizer.from_pretrained(model_repo)
-
-        bnb_config = BitsAndBytesConfig(
-            load_in_4bit = True,
-            bnb_4bit_quant_type = "nf4",
-            bnb_4bit_compute_dtype = torch.float16,
-            bnb_4bit_use_double_quant = True,
-        )
-
-        model = AutoModelForCausalLM.from_pretrained(
-            model_repo,
-            quantization_config = bnb_config,
-            device_map = 'auto',
-            low_cpu_mem_usage = True
-        )
-
-        max_len = 1024
-
-    elif model == 'llama2-7b-chat':
-        model_repo = 'daryl149/llama-2-7b-chat-hf'
-
-        tokenizer = AutoTokenizer.from_pretrained(model_repo, use_fast=True)
-
-        bnb_config = BitsAndBytesConfig(
-            load_in_4bit = True,
-            bnb_4bit_quant_type = "nf4",
-            bnb_4bit_compute_dtype = torch.float16,
-            bnb_4bit_use_double_quant = True,
-        )
-
-        model = AutoModelForCausalLM.from_pretrained(
-            model_repo,
-            quantization_config = bnb_config,
-            device_map = 'auto',
-            low_cpu_mem_usage = True,
-            trust_remote_code = True
-        )
-
-        max_len = 2048
-
-    elif model == 'llama2-13b-chat':
-        model_repo = 'daryl149/llama-2-13b-chat-hf'
-
-        tokenizer = AutoTokenizer.from_pretrained(model_repo, use_fast=True)
-
-        bnb_config = BitsAndBytesConfig(
-            load_in_4bit = True,
-            bnb_4bit_quant_type = "nf4",
-            bnb_4bit_compute_dtype = torch.float16,
-            bnb_4bit_use_double_quant = True,
-        )
-
-        model = AutoModelForCausalLM.from_pretrained(
-            model_repo,
-            quantization_config = bnb_config,
-
-            low_cpu_mem_usage = True,
-            trust_remote_code = True
-        )
-
-        max_len = 2048  #8192
-        truncation=True,  # Explicitly enable truncation
-        padding="max_len"  # Optional: pad to max_length
-
-    elif model == 'mistral-7B':
-        model_repo = 'mistralai/Mistral-7B-v0.1'
-
-        tokenizer = AutoTokenizer.from_pretrained(model_repo)
-
-        bnb_config = BitsAndBytesConfig(
-            load_in_4bit = True,
-            bnb_4bit_quant_type = "nf4",
-            bnb_4bit_compute_dtype = torch.float16,
-            bnb_4bit_use_double_quant = True,
-        )
-
-        model = AutoModelForCausalLM.from_pretrained(
-            model_repo,
-            quantization_config = bnb_config,
-            device_map = 'auto',
-            low_cpu_mem_usage = True,
-        )
-
-        max_len = 1024
-
-    else:
-        print("Not implemented model (tokenizer and backbone)")
-
-    return tokenizer, model, max_len
-
-def get_model(model = CFG.model_name):
-
-
-    if model == 'wizardlm':
-        model_repo = 'TheBloke/wizardLM-7B-HF'
-
-        tokenizer = AutoTokenizer.from_pretrained(model_repo)
-
-        bnb_config = BitsAndBytesConfig(
-            load_in_4bit = True,
-            bnb_4bit_quant_type = "nf4",
-            bnb_4bit_compute_dtype = torch.float16,
-            bnb_4bit_use_double_quant = True,
-        )
-
-        model = AutoModelForCausalLM.from_pretrained(
-            model_repo,
-            quantization_config = bnb_config,
-            device_map = 'auto',
-            low_cpu_mem_usage = True
-        )
-
-        max_len = 1024
-
-    elif model == 'llama2-7b-chat':
-        model_repo = 'daryl149/llama-2-7b-chat-hf'
-
-        tokenizer = AutoTokenizer.from_pretrained(model_repo, use_fast=True)
-
-        bnb_config = BitsAndBytesConfig(
-            load_in_4bit = True,
-            bnb_4bit_quant_type = "nf4",
-            bnb_4bit_compute_dtype = torch.float16,
-            bnb_4bit_use_double_quant = True,
-        )
-
-        model = AutoModelForCausalLM.from_pretrained(
-            model_repo,
-            quantization_config = bnb_config,
-            device_map = 'auto',
-            low_cpu_mem_usage = True,
-            trust_remote_code = True
-        )
-
-        max_len = 2048
-
-    elif model == 'llama2-13b-chat':
-        model_repo = 'daryl149/llama-2-13b-chat-hf'
-
-        tokenizer = AutoTokenizer.from_pretrained(model_repo, use_fast=True)
-
-        bnb_config = BitsAndBytesConfig(
-            load_in_4bit = True,
-            bnb_4bit_quant_type = "nf4",
-            bnb_4bit_compute_dtype = torch.float16,
-            bnb_4bit_use_double_quant = True,
-        )
-
-        model = AutoModelForCausalLM.from_pretrained(
-            model_repo,
-            quantization_config = bnb_config,
-
-            low_cpu_mem_usage = True,
-            trust_remote_code = True
-        )
-
-        max_len = 2048  #8192
-        truncation=True,  # Explicitly enable truncation
-        padding="max_len"  # Optional: pad to max_length
-
-    elif model == 'mistral-7B':
-        model_repo = 'mistralai/Mistral-7B-v0.1'
-
-        tokenizer = AutoTokenizer.from_pretrained(model_repo)
-
-        bnb_config = BitsAndBytesConfig(
-            load_in_4bit = True,
-            bnb_4bit_quant_type = "nf4",
-            bnb_4bit_compute_dtype = torch.float16,
-            bnb_4bit_use_double_quant = True,
-        )
-
-        model = AutoModelForCausalLM.from_pretrained(
-            model_repo,
-            quantization_config = bnb_config,
-            device_map = 'auto',
-            low_cpu_mem_usage = True,
-        )
-
-        max_len = 1024
-
-    else:
-        print("Not implemented model (tokenizer and backbone)")
-
-    return tokenizer, model, max_len
-
-
-class CFG:
-    # LLMs
-    model_name = 'llama2-13b-chat' # wizardlm, llama2-7b-chat, llama2-13b-chat, mistral-7B
-    temperature = 0
-    top_p = 0.95
-    repetition_penalty = 1.15
-
-    # splitting
-    split_chunk_size = 800
-    split_overlap = 0
-
-    # embeddings
-    embeddings_model_repo = 'sentence-transformers/all-MiniLM-L6-v2'
-
-    # similar passages
-    k = 6
-
-    # paths
-    PDFs_path = '/content/'
-    Embeddings_path =  '/content/faiss-hp-sentence-transformers'
-    Output_folder = './rag-vectordb'
-
-
-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,
-#     do_sample = True,
-    max_length = max_len,
-    temperature = CFG.temperature,
-    top_p = CFG.top_p,
-    repetition_penalty = CFG.repetition_penalty
-)
-
-### langchain pipeline
-llm = HuggingFacePipeline(pipeline = pipe)
-
-loader = DirectoryLoader(
-    CFG.PDFs_path,
-    glob="./*.pdf",
-    loader_cls=PyPDFLoader,
-    show_progress=True,
-    use_multithreading=True
-)
-
-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,
-    HuggingFaceEmbeddings(model_name='sentence-transformers/all-mpnet-base-v2')
-)
-
-### persist vector database
-vectordb.save_local(f"{CFG.Output_folder}/faiss_index_rag") # save in output folder
-#     vectordb.save_local(f"{CFG.Embeddings_path}/faiss_index_hp") # save in input folder
-
-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"]
-)
-
-retriever = vectordb.as_retriever(search_kwargs = {"k": CFG.k, "search_type" : "similarity"})
-
-qa_chain = RetrievalQA.from_chain_type(
-    llm = llm,
-    chain_type = "stuff", # map_reduce, map_rerank, stuff, refine
-    retriever = retriever,
-    chain_type_kwargs = {"prompt": PROMPT},
-    return_source_documents = True,
-    verbose = False
-)
-
-def start_demo():
-    demo = gr.ChatInterface(
-        predict,
-        title=f'Open-Source LLM ({CFG.model_name}) Question Answering'
-    )
-    demo.queue()
-    demo.launch()
-