241 lines
8.5 KiB
Python
241 lines
8.5 KiB
Python
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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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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# Timeout-Strategie
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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.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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.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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}
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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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# --- API FUNCTIONS ---
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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.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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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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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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# --- 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.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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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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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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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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st.markdown("#### 📚 Verwendete Quellen")
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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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# 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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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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# 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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# 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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# '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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# --- MAIN APP ---
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top_k, show_explain = render_sidebar()
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st.title("mindnet v2.3.1")
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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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# Antwort-Text
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st.markdown(msg["content"])
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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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# Globales Feedback (Sterne)
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qid = msg["query_id"]
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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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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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else:
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st.markdown(msg["content"])
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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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# 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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}) |