File size: 7,222 Bytes
c58df45 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 |
import streamlit as st
import logging
from streamlit_chat import message
from .semantic_process import process_semantic_analysis
from ..chatbot.chatbot import initialize_chatbot, process_semantic_chat_input
from ..database.database_oldFromV2 import store_file_semantic_contents, retrieve_file_contents, delete_file, get_user_files
from ..utils.widget_utils import generate_unique_key
logger = logging.getLogger(__name__)
def get_translation(t, key, default):
return t.get(key, default)
def display_semantic_interface(lang_code, nlp_models, t):
# Inicializar el chatbot y el historial del chat al principio de la funci贸n
if 'semantic_chatbot' not in st.session_state:
st.session_state.semantic_chatbot = initialize_chatbot('semantic')
if 'messages' not in st.session_state:
st.session_state.messages = []
st.markdown("""
<style>
.semantic-initial-message {
background-color: #f0f2f6;
border-left: 5px solid #4CAF50;
padding: 10px;
border-radius: 5px;
font-size: 16px;
margin-bottom: 20px;
}
</style>
""", unsafe_allow_html=True)
# Mostrar el mensaje inicial como un p谩rrafo estilizado
st.markdown(f"""
<div class="morpho-initial-message">
{t['semantic_initial_message']}
</div>
""", unsafe_allow_html=True)
st.title("Semantic Analysis")
# Crear dos columnas principales: una para el chat y otra para la visualizaci贸n
chat_col, viz_col = st.columns([1, 1])
with chat_col:
st.subheader("Chat with AI")
# Contenedor para los mensajes del chat
chat_container = st.container()
# Input para el chat
user_input = st.text_input("Type your message here...", key=generate_unique_key('semantic', 'chat_input'))
if user_input:
# A帽adir mensaje del usuario
st.session_state.messages.append({"role": "user", "content": user_input})
# Generar respuesta del asistente
if user_input.startswith('/analyze_current'):
response = process_semantic_chat_input(user_input, lang_code, nlp_models[lang_code], st.session_state.get('file_contents', ''))
else:
response = st.session_state.semantic_chatbot.generate_response(user_input, lang_code, context=st.session_state.get('file_contents', ''))
# A帽adir respuesta del asistente
st.session_state.messages.append({"role": "assistant", "content": response})
# Mostrar mensajes en el contenedor del chat
with chat_container:
for i, msg in enumerate(st.session_state.messages):
message(msg['content'], is_user=msg['role'] == 'user', key=f"{i}_{msg['role']}")
# Bot贸n para limpiar el chat
if st.button("Clear Chat", key=generate_unique_key('semantic', 'clear_chat')):
st.session_state.messages = []
st.rerun()
with viz_col:
st.subheader("Visualization")
# Selector de archivo y bot贸n de an谩lisis
user_files = get_user_files(st.session_state.username, 'semantic')
file_options = [get_translation(t, 'select_saved_file', 'Select a saved file')] + [file['file_name'] for file in user_files]
selected_file = st.selectbox("Select a file to analyze", options=file_options, key=generate_unique_key('semantic', 'file_selector'))
if st.button("Analyze Document", key=generate_unique_key('semantic', 'analyze_document')):
if selected_file and selected_file != get_translation(t, 'select_saved_file', 'Select a saved file'):
file_contents = retrieve_file_contents(st.session_state.username, selected_file, 'semantic')
if file_contents:
st.session_state.file_contents = file_contents
with st.spinner("Analyzing..."):
try:
nlp_model = nlp_models[lang_code]
concept_graph, entity_graph, key_concepts = process_semantic_analysis(file_contents, nlp_model, lang_code)
st.session_state.concept_graph = concept_graph
st.session_state.entity_graph = entity_graph
st.session_state.key_concepts = key_concepts
st.success("Analysis completed successfully")
except Exception as e:
logger.error(f"Error during analysis: {str(e)}")
st.error(f"Error during analysis: {str(e)}")
else:
st.error("Error loading file contents")
else:
st.error("Please select a file to analyze")
# Visualizaci贸n de conceptos clave
if 'key_concepts' in st.session_state:
st.write("Key Concepts:")
st.write(', '.join([f"{concept}: {freq:.2f}" for concept, freq in st.session_state.key_concepts]))
# Pesta帽as para los gr谩ficos
tab_concept, tab_entity = st.tabs(["Concept Graph", "Entity Graph"])
with tab_concept:
if 'concept_graph' in st.session_state:
st.pyplot(st.session_state.concept_graph)
else:
st.info("No concept graph available. Please analyze a document first.")
with tab_entity:
if 'entity_graph' in st.session_state:
st.pyplot(st.session_state.entity_graph)
else:
st.info("No entity graph available. Please analyze a document first.")
# Secci贸n de carga de archivos
st.subheader("File Management")
uploaded_file = st.file_uploader("Choose a file to upload", type=['txt', 'pdf', 'docx', 'doc', 'odt'], key=generate_unique_key('semantic', 'file_uploader'))
if uploaded_file is not None:
file_contents = uploaded_file.getvalue().decode('utf-8')
if store_file_semantic_contents(st.session_state.username, uploaded_file.name, file_contents):
st.success(f"File {uploaded_file.name} uploaded and saved successfully")
else:
st.error("Error uploading file")
st.markdown("---")
# Gesti贸n de archivos cargados
st.subheader("Manage Uploaded Files")
user_files = get_user_files(st.session_state.username, 'semantic')
if user_files:
for file in user_files:
col1, col2 = st.columns([3, 1])
with col1:
st.write(file['file_name'])
with col2:
if st.button("Delete", key=f"delete_{file['file_name']}", help=f"Delete {file['file_name']}"):
if delete_file(st.session_state.username, file['file_name'], 'semantic'):
st.success(f"File {file['file_name']} deleted successfully")
st.rerun()
else:
st.error(f"Error deleting file {file['file_name']}")
else:
st.info("No files uploaded yet.") |