APP

!pip install gradio --quiet
!pip install xformer --quiet
!pip install chromadb --quiet
!pip install langchain --quiet
!pip install accelerate --quiet
!pip install transformers --quiet
!pip install bitsandbytes --quiet
!pip install unstructured --quiet
!pip install sentence-transformers --quiet

import torch
import gradio as gr

from textwrap import fill
from IPython.display import Markdown, display

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
warnings.filterwarnings('ignore')



MODEL_NAME = "mistralai/Mistral-7B-Instruct-v0.1"

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 = 0.001
generation_config.top_p = 0.95
generation_config.do_sample = True
generation_config.repetition_penalty = 1.15

pipeline = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    return_full_text=True,
    generation_config=generation_config,
)


llm = HuggingFacePipeline(
    pipeline=pipeline,
    )


embeddings = HuggingFaceEmbeddings(
    model_name="thenlper/gte-large",
    model_kwargs={"device": "cuda"},
    encode_kwargs={"normalize_embeddings": True})



urls = [
    "https://www.expansion.com/mercados/cotizaciones/valores/telefonica_M.TEF.html ",
    "https://www.expansion.com/mercados/cotizaciones/valores/bbva_M.BBVA.html ",
    "https://www.expansion.com/mercados/cotizaciones/valores/iberdrola_M.IBE.html",
    "https://www.expansion.com/mercados/cotizaciones/valores/santander_M.SAN.html",
    "https://www.expansion.com/mercados/cotizaciones/valores/ferrovial_M.FER.html",
    "https://www.expansion.com/mercados/cotizaciones/valores/enagas_M.ENG.html",
    "https://www.euroland.com/SiteFiles/market/search.asp?GUID=B8D60F4600CAF1479E480C0BA6CE775E&ViewPageNumber=1&ViewAllStockSelected=False&Operation=selection&SortWinLoser=False&SortDirection=&ColumnToSort=&ClickedWinLoser=&ClickedMarkCap=&NameSearch=&UpperLevel=&LowerLevel=&RegionalIndustry=&RegionalListName=&RegionalListID=&RegionalIndexName=&CorporateSites=False&SharesPerPage=50",
    "https://www.expansion.com/mercados/cotizaciones/indices/ibex35_I.IB.html",
    "https://es.investing.com/equities/telefonica-cash-flow",
    "https://es.investing.com/equities/grupo-ferrovial-cash-flow",
    "https://es.investing.com/equities/bbva-cash-flow",
    "https://es.investing.com/equities/banco-santander-cash-flow",
    "https://es.investing.com/equities/iberdrola-cash-flow",
    "https://es.investing.com/equities/enagas-cash-flow",
    "https://es.investing.com/equities/enagas-ratios",
    "https://es.investing.com/equities/telefonica-ratios",
    "https://es.investing.com/equities/grupo-ferrovial-ratios",
    "https://es.investing.com/equities/bbva-ratios",
    "https://es.investing.com/equities/banco-santander-ratios",
    "https://es.investing.com/equities/iberdrola-ratios"

]

loader = UnstructuredURLLoader(urls=urls)
documents = loader.load()

len(documents)


text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=64)
texts_chunks = text_splitter.split_documents(documents)

len(texts_chunks)
# output: 21

template = """
[INST] <>
Actúa como un bot financiero experto en el análsis de valores cotizados en el IBEX-35
<>

{context}

{question} [/INST]
"""

prompt = PromptTemplate(template=template, input_variables=["context", "question"])

qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=db.as_retriever(search_kwargs={"k": 2}),
    return_source_documents=True,
    chain_type_kwargs={"prompt": prompt},
)

query = "¿Cuál es el precio de la acción de BBVA hoy?"
result_ = qa_chain(
    query
)
result = result_["result"].strip()


display(Markdown(f"<b>{query}</b>"))
display(Markdown(f"<p>{result}</p>"))


query = "Haz un análisis técnico de BBVA  para el año 2022"
result_ = qa_chain(
    query
)
result = result_["result"].strip()


display(Markdown(f"<b>{query}</b>"))
display(Markdown(f"<p>{result}</p>"))

result_["source_documents"]

custom_template = """You are finance AI Assistant Given the
following conversation and a follow up question, rephrase the follow up question
to be a standalone question. At the end of standalone question add this
'Answer the question in English language.' 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(custom_template)

memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

qa_chain = ConversationalRetrievalChain.from_llm(
    llm=llm,
    retriever=db.as_retriever(search_kwargs={"k": 2}),
    memory=memory,
    condense_question_prompt=CUSTOM_QUESTION_PROMPT,
)


query = "Haz un análisis técnico definiendo todos los ratios de BBVA  para el año 2021"
result_ = qa_chain({"question": query})
result = result_["answer"].strip()

display(Markdown(f"<b>{query}</b>"))
display(Markdown(f"<p>{result}</p>"))


query = "¿Cuánto han crecido las ventas de Iberdrola en los últimos cinco años?"
result_ = qa_chain({"question": query})
result = result_["answer"].strip()

display(Markdown(f"<b>{query}</b>"))
display(Markdown(f"<p>{result}</p>"))


query = "¿Cuál es el precio medio de la acción de Iberdrola en 2022?"
result_ = qa_chain({"question": query})
result = result_["answer"].strip()

display(Markdown(f"<b>{query}</b>"))
display(Markdown(f"<p>{result}</p>"))

def querying(query, history):
  memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

  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="¿Cuál es el precio de la acción de BBVA hoy?", container=False, scale=7),
    title="RanitaRené",
    theme="soft",
    examples=["¿Cuál es el precio de la acción de BBVA hoy?",
              "Haz un análisis técnico de BBVA  para el año 2022"
                    ],


    cache_examples=True,
    retry_btn="Repetir",
    undo_btn="Deshacer",
    clear_btn="Borrar",
    submit_btn="Enviar"

    )

iface.launch(share=True)