Update app.py

app.py

@@ -2,17 +2,20 @@ import streamlit as st
 import os
 from PyPDF2 import PdfReader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings 
+from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.vectorstores import FAISS
 from langchain.chat_models import ChatOpenAI
 from langchain.chains.question_answering import load_qa_chain
 
-st.set_page_config('Mi tech personal')
+st.set_page_config(page_title='Mi tech personal', page_icon=':books:')
 st.header("Pregunta a tu teach")
+
+# Configurar la clave de API de OpenAI
 OPENAI_API_KEY = st.text_input('OpenAI API Key', type='password')
-pdf_obj = st.file_uploader("Carga tu documento", type="pdf", on_change=st.cache_resource.clear)
+os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
 
-@st.cache_resource 
+# Cargar y procesar el PDF
+@st.cache_resource
 def create_embeddings(pdf):
     pdf_reader = PdfReader(pdf)
     text = ""
@@ -23,7 +26,7 @@ def create_embeddings(pdf):
         chunk_size=800,
         chunk_overlap=100,
         length_function=len
-        )        
+    )
     chunks = text_splitter.split_text(text)
 
     embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
@@ -31,15 +34,20 @@ def create_embeddings(pdf):
 
     return knowledge_base
 
-if pdf_obj:
-    knowledge_base = create_embeddings(pdf_obj)
-    user_question = st.text_input("Haz una pregunta sobre tu PDF:")
-
-    if user_question:
-        os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
-        docs = knowledge_base.similarity_search(user_question, 3)
-        llm = ChatOpenAI(model_name='gpt-3.5-turbo')
-        chain = load_qa_chain(llm, chain_type="stuff")
-        respuesta = chain.run(input_documents=docs, question=user_question)
+# Cargar el archivo PDF
+pdf_obj = st.file_uploader("Carga tu documento", type="pdf", on_change=st.cache_resource.clear)
 
-        st.write(respuesta)  
+if pdf_obj:
+    try:
+        knowledge_base = create_embeddings(pdf_obj)
+        user_question = st.text_input("Haz una pregunta sobre tu PDF:")
+
+        if user_question:
+            docs = knowledge_base.similarity_search(user_question, 3)
+            llm = ChatOpenAI(model_name='gpt-3.5-turbo')
+            chain = load_qa_chain(llm, chain_type="stuff")
+            respuesta = chain.run(input_documents=docs, question=user_question)
+
+            st.write(respuesta)
+    except Exception as e:
+        st.error(f"Se produjo un error: {e}")