vorbereitung UI für WP07
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ec33163d98
commit
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@ -2,240 +2,214 @@ import streamlit as st
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import requests
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import uuid
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import os
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import time
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import json
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from pathlib import Path
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from dotenv import load_dotenv
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# --- CONFIGURATION ---
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load_dotenv()
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API_BASE_URL = os.getenv("MINDNET_API_URL", "http://localhost:8002")
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CHAT_ENDPOINT = f"{API_BASE_URL}/chat"
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FEEDBACK_ENDPOINT = f"{API_BASE_URL}/feedback"
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HISTORY_FILE = Path("data/logs/search_history.jsonl")
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# Timeout-Strategie
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# Timeout Strategy
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timeout_setting = os.getenv("MINDNET_API_TIMEOUT") or os.getenv("MINDNET_LLM_TIMEOUT")
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API_TIMEOUT = float(timeout_setting) if timeout_setting else 300.0
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# --- PAGE SETUP ---
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st.set_page_config(
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page_title="mindnet v2.3.1",
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page_icon="🧠",
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layout="centered"
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)
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st.set_page_config(page_title="mindnet v2.3.1", page_icon="🧠", layout="wide")
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# --- CSS STYLING (VISUAL POLISH) ---
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st.markdown("""
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<style>
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.reportview-container { margin-top: -2em; }
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.stDeployButton {display:none;}
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/* Hauptcontainer enger machen für Lesbarkeit */
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.block-container { padding-top: 2rem; max_width: 900px; margin: auto; }
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/* Intent Badges */
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.intent-badge {
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background-color: #f0f2f6;
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border-radius: 5px;
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padding: 4px 8px;
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font-size: 0.8em;
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color: #555;
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margin-bottom: 10px;
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display: inline-block;
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border: 1px solid #e0e0e0;
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}
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.source-feedback {
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font-size: 0.8em;
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color: #888;
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background-color: #e8f0fe; color: #1a73e8;
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padding: 4px 10px; border-radius: 12px;
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font-size: 0.8rem; font-weight: 600;
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border: 1px solid #d2e3fc; display: inline-block; margin-bottom: 0.5rem;
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}
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/* Chat Message Styling */
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.stChatMessage { padding: 1rem; border-radius: 8px; margin-bottom: 1rem;}
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div[data-testid="stChatMessageContent"] p { font-size: 1.05rem; line-height: 1.6; }
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/* Expander Cleaner */
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.streamlit-expanderHeader { font-size: 0.9rem; font-weight: 600; color: #444; }
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</style>
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""", unsafe_allow_html=True)
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# --- SESSION STATE ---
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if "messages" not in st.session_state:
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st.session_state.messages = []
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if "user_id" not in st.session_state:
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st.session_state.user_id = str(uuid.uuid4())
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if "messages" not in st.session_state: st.session_state.messages = []
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if "user_id" not in st.session_state: st.session_state.user_id = str(uuid.uuid4())
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if "draft_note" not in st.session_state: st.session_state.draft_note = {"title": "", "content": "", "type": "concept"}
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# --- API FUNCTIONS ---
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# --- HELPER FUNCTIONS ---
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def load_history_from_logs(limit=10):
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"""Liest die letzten N Queries aus dem Logfile (WP-04c Data Flywheel)."""
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queries = []
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if HISTORY_FILE.exists():
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try:
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with open(HISTORY_FILE, "r", encoding="utf-8") as f:
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# Datei rückwärts oder komplett lesen (bei großen Logs besser `tail`)
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lines = f.readlines()
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for line in reversed(lines):
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try:
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entry = json.loads(line)
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q = entry.get("query_text")
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if q and q not in queries:
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queries.append(q)
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if len(queries) >= limit: break
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except: continue
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except Exception as e:
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st.sidebar.warning(f"Log-Fehler: {e}")
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return queries
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def send_chat_message(message: str, top_k: int, explain: bool):
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payload = {"message": message, "top_k": top_k, "explain": explain}
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try:
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response = requests.post(CHAT_ENDPOINT, json=payload, timeout=API_TIMEOUT)
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response = requests.post(
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CHAT_ENDPOINT,
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json={"message": message, "top_k": top_k, "explain": explain},
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timeout=API_TIMEOUT
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)
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response.raise_for_status()
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return response.json()
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except requests.exceptions.ReadTimeout:
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return {"error": f"Timeout ({int(API_TIMEOUT)}s). Das lokale LLM rechnet noch."}
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except Exception as e:
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return {"error": str(e)}
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def submit_feedback(query_id: str, node_id: str, score: int, comment: str = None):
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"""Sendet Feedback asynchron."""
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payload = {
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"query_id": query_id,
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"node_id": node_id,
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"score": score,
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"comment": comment
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}
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def submit_feedback(query_id, node_id, score, comment=None):
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try:
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requests.post(FEEDBACK_ENDPOINT, json=payload, timeout=5)
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# Wir nutzen st.toast für dezentes Feedback ohne Rerun
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requests.post(FEEDBACK_ENDPOINT, json={"query_id": query_id, "node_id": node_id, "score": score, "comment": comment}, timeout=2)
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target = "Antwort" if node_id == "generated_answer" else "Quelle"
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st.toast(f"Feedback für {target} gespeichert! (Score: {score})")
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except Exception as e:
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st.error(f"Feedback-Fehler: {e}")
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except: pass
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# --- UI COMPONENTS ---
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def render_sidebar():
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with st.sidebar:
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st.header("⚙️ Konfiguration")
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st.caption(f"Backend: `{API_BASE_URL}`")
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st.title("🧠 mindnet")
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st.caption("v2.3.1 | WP-10 UI")
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st.subheader("Retrieval")
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top_k = st.slider("Quellen Anzahl", 1, 10, 5)
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explain_mode = st.toggle("Explanation Layer", value=True)
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mode = st.radio("Modus", ["💬 Chat", "📝 Neuer Eintrag (WP-07)"], index=0)
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st.divider()
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st.info("WP-10: Advanced Feedback Loop Active")
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if st.button("Reset Chat"):
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st.session_state.messages = []
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st.rerun()
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return top_k, explain_mode
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st.subheader("⚙️ Settings")
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top_k = st.slider("Quellen (Top-K)", 1, 10, 5)
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explain = st.toggle("Explanation Layer", True)
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def render_intent_badge(intent, source):
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icon = "🧠"
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if intent == "EMPATHY": icon = "❤️"
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elif intent == "DECISION": icon = "⚖️"
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elif intent == "CODING": icon = "💻"
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elif intent == "FACT": icon = "📚"
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return f"""<div class="intent-badge">{icon} <b>Intent:</b> {intent} <span style="color:#999">({source})</span></div>"""
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st.divider()
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st.subheader("🕒 Verlauf")
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history = load_history_from_logs(8)
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if not history:
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st.caption("Noch keine Einträge.")
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for q in history:
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if st.button(f"🔎 {q[:30]}...", key=f"hist_{q}", help=q, use_container_width=True):
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# Trick: Query in Input 'injecten' geht schwer, wir feuern direkt ab
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st.session_state.messages.append({"role": "user", "content": q})
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st.rerun()
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def render_sources(sources, query_id):
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"""
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Rendert Quellen inklusive granularem Feedback-Mechanismus (1-5 via Faces).
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"""
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if not sources:
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return
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return mode, top_k, explain
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st.markdown("#### 📚 Verwendete Quellen")
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def render_chat_interface(top_k, explain):
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# Render History
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for msg in st.session_state.messages:
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with st.chat_message(msg["role"]):
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if msg["role"] == "assistant":
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# Intent Badge
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if "intent" in msg:
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icon = {"EMPATHY": "❤️", "DECISION": "⚖️", "CODING": "💻", "FACT": "📚"}.get(msg["intent"], "🧠")
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st.markdown(f'<div class="intent-badge">{icon} Intent: {msg["intent"]}</div>', unsafe_allow_html=True)
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for idx, hit in enumerate(sources):
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score = hit.get('total_score', 0)
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node_id = hit.get('node_id')
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title = hit.get('note_id', 'Unbekannt')
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payload = hit.get('payload', {})
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note_type = payload.get('type', 'unknown')
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st.markdown(msg["content"])
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# Icon basierend auf Score
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score_icon = "🟢" if score > 0.8 else "🟡" if score > 0.5 else "⚪"
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expander_title = f"{score_icon} {title} (Typ: {note_type}, Score: {score:.2f})"
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# Sources
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if "sources" in msg:
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for hit in msg["sources"]:
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score = hit.get('total_score', 0)
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icon = "🟢" if score > 0.8 else "🟡" if score > 0.5 else "⚪"
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with st.expander(f"{icon} {hit.get('note_id', '?')} ({score:.2f})"):
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st.markdown(f"_{hit.get('source', {}).get('text', '')[:300]}..._")
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if hit.get('explanation'):
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st.caption(f"Grund: {hit['explanation']['reasons'][0]['message']}")
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with st.expander(expander_title):
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# 1. Inhalt
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text = hit.get('source', {}).get('text', 'Kein Text')
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st.markdown(f"_{text[:300]}..._")
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# Granular Feedback
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def _cb(qid=msg["query_id"], nid=hit['node_id']):
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val = st.session_state.get(f"fb_src_{qid}_{nid}")
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if val is not None: submit_feedback(qid, nid, val+1, "Faces UI")
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# 2. Explanation (Why-Layer)
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if 'explanation' in hit and hit['explanation']:
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st.caption("**Warum gefunden?**")
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for r in hit['explanation'].get('reasons', []):
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st.caption(f"- {r.get('message')}")
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st.feedback("faces", key=f"fb_src_{msg['query_id']}_{hit['node_id']}", on_change=_cb)
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# 3. Granulares Feedback (Source Level) - JETZT MIT NUANCEN
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st.markdown("---")
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c1, c2 = st.columns([2, 2])
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with c1:
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st.caption("Relevanz dieser Quelle:")
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with c2:
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# Callback Wrapper für Source-Feedback
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def on_source_fb(qid=query_id, nid=node_id, k=f"fb_src_{node_id}"):
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val = st.session_state.get(k)
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# Mapping:
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# Faces liefert 0 (😞) bis 4 (😀).
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# Wir mappen das auf 1-5 für das Backend.
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if val is not None:
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submit_feedback(qid, nid, val + 1, comment="Source Feedback (Faces)")
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# Global Feedback
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qid = msg["query_id"]
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st.feedback("stars", key=f"fb_glob_{qid}", on_change=lambda: submit_feedback(qid, "generated_answer", st.session_state[f"fb_glob_{qid}"]+1))
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# 'faces' bietet 5 Stufen: 😞(1) 🙁(2) 😐(3) 🙂(4) 😀(5)
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st.feedback(
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"faces",
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key=f"fb_src_{query_id}_{node_id}",
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on_change=on_source_fb,
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kwargs={"qid": query_id, "nid": node_id, "k": f"fb_src_{query_id}_{node_id}"}
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)
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else:
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st.markdown(msg["content"])
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# --- MAIN APP ---
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# Input Logic
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# Prüfen ob wir aus der History kommen (letzte Nachricht User und noch keine Antwort?)
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last_msg_is_user = len(st.session_state.messages) > 0 and st.session_state.messages[-1]["role"] == "user"
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# Da st.rerun() die ganze App neu lädt, müssen wir prüfen, ob wir auf eine Antwort warten
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# Aber Streamlit Flow ist: Input -> Rerun -> Code läuft -> Render.
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# Wir brauchen einen Trigger.
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top_k, show_explain = render_sidebar()
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st.title("mindnet v2.3.1")
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if prompt := st.chat_input("Frage Mindnet..."):
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st.session_state.messages.append({"role": "user", "content": prompt})
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st.rerun()
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# 1. Chat History Rendern
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for msg in st.session_state.messages:
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with st.chat_message(msg["role"]):
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if msg["role"] == "assistant":
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# Meta-Daten
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if "intent" in msg:
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st.markdown(render_intent_badge(msg["intent"], msg.get("intent_source", "?")), unsafe_allow_html=True)
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# Wenn die letzte Nachricht vom User ist (egal ob via Input oder History Button), generiere Antwort
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if last_msg_is_user:
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last_prompt = st.session_state.messages[-1]["content"]
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with st.chat_message("assistant"):
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with st.spinner("Thinking..."):
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resp = send_chat_message(last_prompt, top_k, explain)
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# Antwort-Text
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st.markdown(msg["content"])
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if "error" in resp:
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st.error(resp["error"])
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# Entferne die User Nachricht, damit man es nochmal probieren kann? Optional.
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else:
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# Speichern und Rerun für sauberes Rendering
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msg_data = {
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"role": "assistant",
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"content": resp.get("answer"),
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"intent": resp.get("intent", "FACT"),
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"sources": resp.get("sources", []),
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"query_id": resp.get("query_id")
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}
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st.session_state.messages.append(msg_data)
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st.rerun()
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# Quellen (mit Feedback-Option, aber Status ist readonly für alte Nachrichten in Streamlit oft schwierig,
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# daher rendern wir Feedback-Controls idealerweise nur für die letzte Nachricht oder speichern Status)
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# In dieser Version rendern wir sie immer, Streamlit State managed das.
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if "sources" in msg:
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render_sources(msg["sources"], msg["query_id"])
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def render_creation_interface():
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st.header("📝 Neuer Wissens-Eintrag (WP-07/11)")
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st.info("Hier kannst du strukturierte Notizen erstellen, die direkt in den Obsidian Vault gespeichert werden.")
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# Globales Feedback (Sterne)
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qid = msg["query_id"]
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with st.form("new_entry"):
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col1, col2 = st.columns([3, 1])
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title = col1.text_input("Titel der Notiz", placeholder="z.B. Projekt Gamma Meeting")
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n_type = col2.selectbox("Typ", ["concept", "meeting", "person", "project", "decision"])
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def on_global_fb(q=qid, k=f"fb_glob_{qid}"):
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val = st.session_state.get(k) # Liefert 0-4
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if val is not None:
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submit_feedback(q, "generated_answer", val + 1, comment="Global Star Rating")
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content = st.text_area("Inhalt (Markdown)", height=300, placeholder="# Protokoll\n\n- Punkt 1...")
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st.caption("Wie gut war diese Antwort?")
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st.feedback(
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"stars",
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key=f"fb_glob_{qid}",
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on_change=on_global_fb
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)
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st.markdown("**Automatische Vernetzung:**")
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st.caption("Verwende `[[Link]]` für Referenzen und `[[rel:depends_on X]]` für logische Kanten.")
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else:
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st.markdown(msg["content"])
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submitted = st.form_submit_button("💾 Speichern & Indizieren")
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if submitted:
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# TODO: Hier müsste der POST Request an eine /ingest API gehen
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# Da diese API in v2.3.1 noch fehlt, simulieren wir es.
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st.success(f"Mockup: Notiz '{title}' ({n_type}) wäre jetzt gespeichert worden!")
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st.balloons()
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# 2. User Input
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if prompt := st.chat_input("Deine Frage an das System..."):
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# User Message anzeigen
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st.session_state.messages.append({"role": "user", "content": prompt})
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with st.chat_message("user"):
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st.markdown(prompt)
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# --- MAIN LOOP ---
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mode, top_k, explain = render_sidebar()
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# API Call
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with st.chat_message("assistant"):
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with st.spinner("Thinking..."):
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resp = send_chat_message(prompt, top_k, show_explain)
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if "error" in resp:
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st.error(resp["error"])
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else:
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# Daten extrahieren
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answer = resp.get("answer", "")
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intent = resp.get("intent", "FACT")
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source = resp.get("intent_source", "Unknown")
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query_id = resp.get("query_id")
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hits = resp.get("sources", [])
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# Sofort rendern (damit User nicht auf Rerun warten muss)
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st.markdown(render_intent_badge(intent, source), unsafe_allow_html=True)
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st.markdown(answer)
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render_sources(hits, query_id)
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# Feedback Slot für die NEUE Nachricht vorbereiten
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st.caption("Wie gut war diese Antwort?")
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st.feedback("stars", key=f"fb_glob_{query_id}", on_change=lambda: submit_feedback(query_id, "generated_answer", st.session_state[f"fb_glob_{query_id}"] + 1))
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# In History speichern
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st.session_state.messages.append({
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"role": "assistant",
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"content": answer,
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"intent": intent,
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"intent_source": source,
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"sources": hits,
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"query_id": query_id
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})
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if mode == "💬 Chat":
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render_chat_interface(top_k, explain)
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else:
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render_creation_interface()
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