Update app.py

app.py

@@ -1,46 +1,60 @@
 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 HuggingFaceHubEmbeddings  # Cambiado desde 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('preguntaDOC')
+st.set_page_config(page_title='preguntaDOC')
 st.header("Pregunta a tu PDF")
+
 OPENAI_API_KEY = st.text_input('OpenAI API Key', type='password')
+HUGGINGFACE_API_KEY = st.text_input('Hugging Face API Key', type='password')  # Añadido para la API de Hugging Face
+
 pdf_obj = st.file_uploader("Carga tu documento", type="pdf", on_change=st.cache_resource.clear)
 
 @st.cache_resource 
-def create_embeddings(pdf):
+def create_embeddings(pdf, hf_api_key):
+    os.environ["HUGGINGFACEHUB_API_TOKEN"] = hf_api_key  # Configurar token de HF
+    
     pdf_reader = PdfReader(pdf)
     text = ""
     for page in pdf_reader.pages:
         text += page.extract_text()
-
+    
     text_splitter = RecursiveCharacterTextSplitter(
         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")
+    
+    # Usar HuggingFaceHubEmbeddings en lugar de HuggingFaceEmbeddings
+    embeddings = HuggingFaceHubEmbeddings(
+        repo_id="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+        huggingfacehub_api_token=hf_api_key
+    )
+    
     knowledge_base = FAISS.from_texts(chunks, embeddings)
-
     return knowledge_base
 
-if pdf_obj:
-    knowledge_base = create_embeddings(pdf_obj)
+if pdf_obj and HUGGINGFACE_API_KEY:
+    knowledge_base = create_embeddings(pdf_obj, HUGGINGFACE_API_KEY)
     user_question = st.text_input("Haz una pregunta sobre tu PDF:")
-
-    if user_question:
+    
+    if user_question and OPENAI_API_KEY:
         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)
-
-        st.write(respuesta)    
+        
+        with st.spinner("Procesando tu pregunta..."):
+            try:
+                respuesta = chain.run(input_documents=docs, question=user_question)
+                st.write(respuesta)
+            except Exception as e:
+                st.error(f"Error: {str(e)}")
+elif pdf_obj and not HUGGINGFACE_API_KEY:
+    st.warning("Por favor, introduce una clave API de Hugging Face para procesar el documento.")