Update app.py

app.py

@@ -1,19 +1,24 @@
 import streamlit as st
 from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, AutoConfig, AutoModelForSequenceClassification
-from langchain.llms import HuggingFacePipeline
+from langchain_community.llms import HuggingFacePipeline
 from langchain.prompts import PromptTemplate
 from langchain.chains import LLMChain
-from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from langchain_community.embeddings import HuggingFaceEmbeddings
 from PyPDF2 import PdfReader
 from docx import Document
 import csv
 import json
 import torch
-from langchain.vectorstores import FAISS
+from langchain_community.vectorstores import FAISS
 from langchain.text_splitter import RecursiveCharacterTextSplitter
+from huggingface_hub import login
+
+# Autenticación en Hugging Face
+huggingface_token = st.secrets["HUGGINGFACE_TOKEN"]
+login(huggingface_token)
 
 # Configurar modelo y tokenizador
-model_name = 'mistralai/Mistral-7B-Instruct-v0.3'
+model_name = 'mistralai/Mistral-7B-Instruct-v0.1'
 model_config = AutoConfig.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
 tokenizer.pad_token = tokenizer.eos_token
@@ -173,17 +178,20 @@ def main():
             document_text = handle_uploaded_file(uploaded_file)
             if operation == "Traducir":
                 target_language = st.selectbox("Selecciona el idioma de traducción", ["español", "inglés", "francés", "alemán"])
-                bot_response = translate(document_text, target_language)
+                if target_language:
+                    bot_response = translate(document_text, target_language)
+                    st.write(f"**Assistant:** {bot_response}")
             elif operation == "Resumir":
                 summary_length = st.selectbox("Selecciona la longitud del resumen", ["corto", "medio", "largo"])
-                if summary_length == "corto":
-                    length = "de aproximadamente 50 palabras"
-                elif summary_length == "medio":
-                    length = "de aproximadamente 100 palabras"
-                elif summary_length == "largo":
-                    length = "de aproximadamente 500 palabras"
-                bot_response = summarize(document_text, length)
-            st.write(f"**Assistant:** {bot_response}")
+                if summary_length:
+                    if summary_length == "corto":
+                        length = "de aproximadamente 50 palabras"
+                    elif summary_length == "medio":
+                        length = "de aproximadamente 100 palabras"
+                    elif summary_length == "largo":
+                        length = "de aproximadamente 500 palabras"
+                    bot_response = summarize(document_text, length)
+                    st.write(f"**Assistant:** {bot_response}")
 
 if __name__ == "__main__":
     main()