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"""
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Componenti UI riutilizzabili per Streamlit.
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"""
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import streamlit as st
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import pandas as pd
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from typing import Dict
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from config import Config
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def setup_page_config():
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"""Configura la pagina Streamlit"""
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st.set_page_config(
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page_title="Anonimizzatore Documenti",
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page_icon="🔒",
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layout="wide"
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)
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def display_sidebar():
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"""Mostra sidebar con configurazioni"""
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with st.sidebar:
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st.header("⚙️ Configurazione")
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if Config.AZURE_API_KEY and Config.AZURE_ENDPOINT:
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st.success("✅ Azure OpenAI configurato")
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st.info(f"Chat Model: {Config.DEPLOYMENT_NAME}")
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st.info(f"Embedding Model: {Config.AZURE_EMBEDDING_DEPLOYMENT_NAME}")
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else:
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st.error("❌ Azure OpenAI non configurato")
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st.write("Configura le variabili d'ambiente:")
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st.code("""
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AZURE_ENDPOINT=your_endpoint
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AZURE_API_KEY=your_api_key
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AZURE_ENDPOINT_EMB=your_embedding_endpoint
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AZURE_API_KEY_EMB=your_embedding_api_key
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""")
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st.markdown("---")
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if 'uploaded_files' in st.session_state and st.session_state.uploaded_files:
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st.subheader("📊 Statistiche")
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uploaded_count = len(st.session_state.uploaded_files)
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anonymized_count = len(st.session_state.get('anonymized_docs', {}))
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confirmed_count = sum(1 for doc in st.session_state.get('anonymized_docs', {}).values()
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if doc.get('confirmed', False))
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st.metric("File caricati", uploaded_count)
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st.metric("Anonimizzati", anonymized_count)
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st.metric("Confermati", confirmed_count)
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if confirmed_count > 0:
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if st.session_state.get('vector_store_built', False):
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st.success("✅ Knowledge Base pronto")
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else:
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st.info("🔄 Knowledge Base da costruire")
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st.markdown("---")
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if st.button("🔄 Reset sessione"):
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for key in list(st.session_state.keys()):
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del st.session_state[key]
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st.rerun()
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def display_entity_editor(entities: Dict, doc_key: str):
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"""Editor per entità rilevate"""
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if not entities:
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st.info("Nessuna entità sensibile rilevata.")
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return entities
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st.subheader("🔍 Entità rilevate")
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st.write("Verifica e modifica le entità sensibili:")
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current_entities_list = list(entities.items())
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updated_entities_dict = {}
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deleted_placeholders = set()
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for i, (placeholder, original_value) in enumerate(current_entities_list):
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col1, col2, col3 = st.columns([2, 3, 1])
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with col1:
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st.write(f"**{placeholder}**")
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with col2:
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new_value = st.text_input(
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"Valore originale",
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value=original_value,
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key=f"{doc_key}_{placeholder}_value_{i}"
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)
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updated_entities_dict[placeholder] = new_value
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with col3:
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if st.button("🗑️", key=f"{doc_key}_{placeholder}_delete_{i}", help="Rimuovi"):
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deleted_placeholders.add(placeholder)
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if deleted_placeholders:
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final_entities = {k: v for k, v in updated_entities_dict.items()
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if k not in deleted_placeholders}
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st.session_state.anonymized_docs[doc_key]['entities'] = final_entities
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from anonymizer import NERAnonimizer
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anonymizer = NERAnonimizer()
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st.session_state.anonymized_docs[doc_key]['anonymized'], _ = anonymizer.anonymize(
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st.session_state.anonymized_docs[doc_key]['original']
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)
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st.session_state.vector_store_built = False
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st.rerun()
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return updated_entities_dict
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def display_file_preview(filename: str, content: str, max_chars: int = 500):
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"""Mostra anteprima file"""
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with st.expander(f"📄 {filename} ({len(content)} caratteri)"):
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preview_text = content[:max_chars]
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if len(content) > max_chars:
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preview_text += "..."
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st.text_area(
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"Contenuto",
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value=preview_text,
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height=150,
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disabled=True,
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key=f"preview_{filename}",
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label_visibility="collapsed"
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)
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def display_analysis_results(filename: str, result: Dict):
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"""Mostra risultati analisi"""
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with st.expander(f"📊 Analisi: {filename}"):
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col1, col2, col3 = st.columns(3)
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col1.metric("Caratteri testo", len(result['anonymized_text']))
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col2.metric("Entità trovate", result['entities_count'])
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col3.metric("Stato", "✅ Completato")
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st.subheader("📄 Testo Anonimizzato")
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st.text_area(
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"Testo processato",
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value=result['anonymized_text'],
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height=150,
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disabled=True,
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key=f"analysis_text_{filename}"
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)
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st.subheader("🤖 Analisi AI")
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st.markdown(result['analysis'])
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if result['entities']:
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st.subheader("🔍 Entità Anonimizzate")
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entities_df = pd.DataFrame([
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{
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'Placeholder': k,
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'Valore Originale': v,
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'Tipo': k.split('_')[0].replace('[', '')
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}
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for k, v in result['entities'].items()
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])
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st.dataframe(entities_df, use_container_width=True)
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def display_crewai_result(analysis: Dict, index: int):
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"""Mostra risultato analisi CrewAI"""
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with st.expander(
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f"🤖 Analisi {index}: {analysis['analysis_type'].upper()} - {analysis['timestamp']}"
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):
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col1, col2, col3 = st.columns(3)
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with col1:
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st.metric("Tipo Analisi", analysis['analysis_type'].capitalize())
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with col2:
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st.metric("Timestamp", analysis['timestamp'])
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with col3:
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agents_used = analysis.get('agents_used', 'auto')
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if agents_used == 'auto':
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agent_count = "Automatico"
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elif isinstance(agents_used, list):
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agent_count = f"{len(agents_used)} agenti"
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else:
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agent_count = str(agents_used)
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st.metric("Agenti", agent_count)
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st.subheader("❓ Query Originale")
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st.info(analysis['query'])
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st.subheader("🎯 Risultato Analisi")
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st.markdown(analysis['result'])
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def display_progress_metrics():
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"""Mostra metriche di progresso"""
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if 'anonymized_docs' in st.session_state:
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confirmed_count = sum(1 for doc in st.session_state.anonymized_docs.values()
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if doc.get('confirmed', False))
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total_count = len(st.session_state.anonymized_docs)
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if total_count > 0:
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st.metric(
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"Progresso Conferme",
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f"{confirmed_count}/{total_count}",
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delta=f"{(confirmed_count/total_count)*100:.1f}%"
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)
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def display_examples_section():
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"""Mostra esempi di query CrewAI"""
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with st.expander("💡 Esempi di Query per CrewAI"):
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st.markdown("""
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**Analisi Comprensiva:**
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- "Fornisci un'analisi completa dei documenti identificando rischi, opportunità e raccomandazioni strategiche"
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- "Analizza la comunicazione aziendale e suggerisci miglioramenti nella gestione clienti"
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**Analisi Documentale:**
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- "Classifica i documenti per tipologia e identifica pattern ricorrenti"
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- "Analizza la struttura e organizzazione delle informazioni nei documenti"
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**Sentiment Analysis:**
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- "Valuta il sentiment generale nelle comunicazioni e identifica aree di miglioramento"
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- "Analizza le emozioni e i trend nei feedback dei clienti"
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**Query RAG Avanzata:**
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- "Trova tutte le menzioni di problemi operativi e le relative soluzioni proposte"
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- "Estrai informazioni su scadenze, deadline e milestone importanti"
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**Personalizzata:**
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- Combina agenti specifici per analisi mirate alle tue esigenze
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""")
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def create_download_button(data: str, filename: str, label: str, key: str):
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"""Crea bottone download con dati"""
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st.download_button(
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label=label,
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data=data,
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file_name=filename,
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mime="application/json",
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key=key
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) |
