app.py

import streamlit as st
from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate
from llama_index.llms.huggingface import HuggingFaceInferenceAPI
from dotenv import load_dotenv
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core import Settings
import os
import base64

# Load environment variables
load_dotenv()

# Configure the Llama index settings
Settings.llm = HuggingFaceInferenceAPI(
 model_name="google/gemma-1.1-7b-it",
 tokenizer_name="google/gemma-1.1-7b-it",
 context_window=3000,
 token=os.getenv("HF_TOKEN"),
 max_new_tokens=512,
 generate_kwargs={"temperature": 0.1},
)
Settings.embed_model = HuggingFaceEmbedding(
 model_name="BAAI/bge-small-en-v1.5"
)

# Define the directory for persistent storage and data
PERSIST_DIR = "./db"
DATA_DIR = "data"

# Ensure data directory exists
os.makedirs(DATA_DIR, exist_ok=True)
os.makedirs(PERSIST_DIR, exist_ok=True)

def displayPDF(file):
 with open(file, "rb") as f:
 base64_pdf = base64.b64encode(f.read()).decode('utf-8')
 pdf_display = f'<iframe src="data:application/pdf;base64,{base64_pdf}" width="100%" height="600" type="application/pdf"></iframe>'
 st.markdown(pdf_display, unsafe_allow_html=True)

def data_ingestion():
 documents = SimpleDirectoryReader(DATA_DIR).load_data()
 storage_context = StorageContext.from_defaults()
 index = VectorStoreIndex.from_documents(documents)
 index.storage_context.persist(persist_dir=PERSIST_DIR)

def handle_query(query):
 storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
 index = load_index_from_storage(storage_context)
 chat_text_qa_msgs = [
 (
 "user",
 """You are a Q&A assistant. Your main goal is to provide answers as accurately as possible, based on the instructions and context you have been given. If a question does not match the provided context or is outside the scope of the document, kindly advise the user to ask questions within the context of the document. Provides the answers in Spanish and cite the page and section where the answers were found.
 Context:
 {context_str}
 Question:
 {query_str}
 """
 )
 ]
 text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
 
 query_engine = index.as_query_engine(text_qa_template=text_qa_template)
 answer = query_engine.query(query)
 
 if hasattr(answer, 'response'):
 return answer.response
 elif isinstance(answer, dict) and 'response' in answer:
 return answer['response']
 else:
 return "Disculpa no pude encontrar una respuesta."


# Streamlit app initialization
st.title("(PDF) Chat con documentos de Procesos 🗞️")
st.markdown("Retrieval-Augmented Generation") 
st.markdown("iniciar chat ...🚀")

if 'messages' not in st.session_state:
 st.session_state.messages = [{'role': 'assistant', "content": 'Hola! Selecciona un pdf para cargar, y hazme una pregunta.'}]

with st.sidebar:
 st.image('image_logo.jpeg', use_column_width=True) # Display the company logo at the top of the sidebar
 st.title("Menu:")
 uploaded_file = st.file_uploader("Sube un archivo PDF y dale click al botón enviar y procesar.")
 if st.button("Enviar y Procesar"):
 with st.spinner("Procesando..."):
 filepath = "data/saved_pdf.pdf"
 with open(filepath, "wb") as f:
 f.write(uploaded_file.getbuffer())
 # displayPDF(filepath) # Display the uploaded PDF
 data_ingestion() # Process PDF every time new file is uploaded
 st.success("Done")

user_prompt = st.chat_input("Pregunta acerca del contenido en el archivo PDF:")
if user_prompt:
 st.session_state.messages.append({'role': 'user', "content": user_prompt})
 response = handle_query(user_prompt)
 st.session_state.messages.append({'role': 'assistant', "content": response})

for message in st.session_state.messages:
 with st.chat_message(message['role']):
 st.write(message['content'])