add sentencepiece

app.py

@@ -1,3 +1,4 @@
+import os
 import torch
 import gradio as gr
 from textwrap import fill
@@ -18,11 +19,10 @@ from langchain.chains import LLMChain, SimpleSequentialChain, RetrievalQA, Conve
 from transformers import BitsAndBytesConfig, AutoModelForCausalLM, AutoTokenizer, GenerationConfig, pipeline
 import warnings
 from huggingface_hub import login
-import os
 
 warnings.filterwarnings('ignore')
 
-# Login to Hugging Face using the token from environment variables
+# Ensure Hugging Face token is set in the environment variables
 huggingface_token = os.getenv('huggingface_token')
 login(huggingface_token)
 
@@ -118,123 +118,4 @@ iface = gr.ChatInterface(
     submit_btn="Enviar"
 )
 
-iface.launch(share=True)
-import torch
-import gradio as gr
-from textwrap import fill
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    HumanMessagePromptTemplate,
-    SystemMessagePromptTemplate,
-)
-from langchain import PromptTemplate
-from langchain import HuggingFacePipeline
-from langchain.vectorstores import Chroma
-from langchain.schema import AIMessage, HumanMessage
-from langchain.memory import ConversationBufferMemory
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.document_loaders import UnstructuredMarkdownLoader, UnstructuredURLLoader
-from langchain.chains import LLMChain, SimpleSequentialChain, RetrievalQA, ConversationalRetrievalChain
-from transformers import BitsAndBytesConfig, AutoModelForCausalLM, AutoTokenizer, GenerationConfig, pipeline
-import warnings
-from huggingface_hub import login
-
-warnings.filterwarnings('ignore')
-
-# Login to Hugging Face
-login("your_huggingface_token")
-
-MODEL_NAME = "mistralai/Mistral-7B-Instruct-v0.3"
-EMBEDDING_MODEL = 'sentence-transformers/paraphrase-multilingual-mpnet-base-v2'
-
-quantization_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_compute_dtype=torch.float16,
-    bnb_4bit_quant_type="nf4",
-    bnb_4bit_use_double_quant=True,
-)
-
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=True)
-tokenizer.pad_token = tokenizer.eos_token
-
-model = AutoModelForCausalLM.from_pretrained(
-    MODEL_NAME, torch_dtype=torch.float16,
-    trust_remote_code=True,
-    device_map="auto",
-    quantization_config=quantization_config
-)
-
-generation_config = GenerationConfig.from_pretrained(MODEL_NAME)
-generation_config.max_new_tokens = 1024
-generation_config.temperature = 0.0001
-generation_config.top_p = 0.95
-generation_config.do_sample = True
-generation_config.repetition_penalty = 1.15
-
-llm = HuggingFacePipeline(pipeline=pipeline)
-embeddings = HuggingFaceEmbeddings(model_name = EMBEDDING_MODEL)
-
-urls = [
-    "https://www.boe.es/diario_boe/txt.php?id=BOE-A-2024-9523"
-]
-
-loader = UnstructuredURLLoader(urls=urls)
-documents = loader.load()
-
-text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-texts_chunks = text_splitter.split_documents(documents)
-
-db = Chroma.from_documents(texts_chunks, embeddings, persist_directory="db")
-
-template = """Act as an lawyer assistant manager expert. Use the following information to answer the question at the end.
-'You must always answer in Spanish' If you do not know the answer reply with 'I am sorry, I dont have enough information'.
-Chat History
-{chat_history}
-Follow Up Input: {question}
-Standalone question:
-"""
-
-CUSTOM_QUESTION_PROMPT = PromptTemplate.from_template(template)
-
-memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-
-llm_chain = ConversationalRetrievalChain.from_llm(
-    llm=llm,
-    retriever=db.as_retriever(search_kwargs={"k": 2}),
-    memory=memory,
-    condense_question_prompt=CUSTOM_QUESTION_PROMPT,
-)
-
-def querying(query, history):
-  memory = ConversationBufferMemory(memory_key="chat_history", return_messages=False)
-
-  qa_chain = ConversationalRetrievalChain.from_llm(
-      llm=llm,
-      retriever=db.as_retriever(search_kwargs={"k": 2}),
-      memory=memory,
-      condense_question_prompt=CUSTOM_QUESTION_PROMPT,
-  )
-
-  result = qa_chain({"question": query})
-  return result["answer"].strip()
-
-iface = gr.ChatInterface(
-    fn = querying,
-    chatbot=gr.Chatbot(height=600),
-    textbox=gr.Textbox(placeholder="Cuantos segmentos hay y en que consisten?", container=False, scale=7),
-    title="LawyerBot",
-    theme="soft",
-    examples=["¿Cuantos segmentos hay?",
-              "¿Que importe del bono digital corresponde a cada uno de los 5 segmentos?",
-              "¿Cuál es el importe de la ayuda para el segmento III en canto a dispositivo hardware?",
-              "Si tengo una microempresa de 2 empleado, ¿qué importe del bono digital me corresponde?",
-              "¿Qué nuevos segmentos de beneficiarios se han introducido?"],
-    cache_examples=True,
-    retry_btn="Repetir",
-    undo_btn="Deshacer",
-    clear_btn="Borrar",
-    submit_btn="Enviar"
-)
-
-iface.launch(share=True)
+iface.launch(share=True)

requirements.txt

@@ -11,4 +11,5 @@ sentence-transformers
 langchain-community
 tiktoken
 langchain_experimental
-langchain_openai
+langchain_openai
+sentencepiece