File size: 8,341 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 |
import streamlit as st
import logging
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 al principio de la función
if 'semantic_chatbot' not in st.session_state:
st.session_state.semantic_chatbot = initialize_chatbot('semantic')
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;
}
.stTabs [data-baseweb="tab-list"] {
gap: 24px;
}
.stTabs [data-baseweb="tab"] {
height: 50px;
white-space: pre-wrap;
background-color: #F0F2F6;
border-radius: 4px 4px 0px 0px;
gap: 1px;
padding-top: 10px;
padding-bottom: 10px;
}
.stTabs [aria-selected="true"] {
background-color: #FFFFFF;
}
.file-list {
border: 1px solid #ddd;
border-radius: 5px;
padding: 10px;
margin-top: 20px;
}
.file-item {
display: flex;
justify-content: space-between;
align-items: center;
padding: 5px 0;
border-bottom: 1px solid #eee;
}
.file-item:last-child {
border-bottom: none;
}
.chat-container {
border: 1px solid #ddd;
border-radius: 5px;
padding: 10px;
height: 400px;
overflow-y: auto;
}
.graph-container {
border: 1px solid #ddd;
border-radius: 5px;
padding: 10px;
}
</style>
""", unsafe_allow_html=True)
# Mostrar el mensaje inicial como un párrafo estilizado
st.markdown(f"""
<div class="semantic-initial-message">
{t['semantic_initial_message']}
</div>
""", unsafe_allow_html=True)
tab1, tab2 = st.tabs(["Upload", "Analyze"])
with tab1:
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("---") # Línea separadora
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.")
with tab2:
st.subheader("Select File for Analysis")
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("", 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")
# Chat and Visualization
col_chat, col_graph = st.columns([1, 1])
with col_chat:
st.subheader("Chat with AI")
st.markdown('<div class="chat-container">', unsafe_allow_html=True)
chat_history = st.session_state.get('semantic_chat_history', [])
for message in chat_history:
with st.chat_message(message["role"]):
st.write(message["content"])
st.markdown('</div>', unsafe_allow_html=True)
user_input = st.chat_input("Type your message here...", key=generate_unique_key('semantic', 'chat_input'))
if user_input:
chat_history.append({"role": "user", "content": user_input})
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)
chat_history.append({"role": "assistant", "content": response})
st.session_state.semantic_chat_history = chat_history
if st.button("Clear Chat", key=generate_unique_key('semantic', 'clear_chat')):
st.session_state.semantic_chat_history = []
st.rerun()
with col_graph:
st.subheader("Visualization")
st.markdown('<div class="graph-container">', unsafe_allow_html=True)
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]))
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.")
st.markdown('</div>', unsafe_allow_html=True) |