diff --git a/app/frontend/ui.py b/app/frontend/ui.py index dc80376..d166661 100644 --- a/app/frontend/ui.py +++ b/app/frontend/ui.py @@ -2,22 +2,17 @@ import streamlit as st import requests import uuid import os -import json -from datetime import datetime +import time from dotenv import load_dotenv # --- CONFIGURATION --- -# Load .env file explicitly to get timeouts and URLs load_dotenv() API_BASE_URL = os.getenv("MINDNET_API_URL", "http://localhost:8002") CHAT_ENDPOINT = f"{API_BASE_URL}/chat" FEEDBACK_ENDPOINT = f"{API_BASE_URL}/feedback" -# Timeout strategy: -# 1. Try MINDNET_API_TIMEOUT (specific for frontend) -# 2. Try MINDNET_LLM_TIMEOUT (backend setting) -# 3. Default to 300 seconds (5 minutes) for local inference safety +# Timeout-Strategie timeout_setting = os.getenv("MINDNET_API_TIMEOUT") or os.getenv("MINDNET_LLM_TIMEOUT") API_TIMEOUT = float(timeout_setting) if timeout_setting else 300.0 @@ -28,16 +23,10 @@ st.set_page_config( layout="centered" ) -# Custom CSS for cleaner look st.markdown(""" """, unsafe_allow_html=True) -# --- SESSION STATE INITIALIZATION --- +# --- SESSION STATE --- if "messages" not in st.session_state: st.session_state.messages = [] if "user_id" not in st.session_state: st.session_state.user_id = str(uuid.uuid4()) -# --- API CLIENT FUNCTIONS --- +# --- API FUNCTIONS --- def send_chat_message(message: str, top_k: int, explain: bool): - """Sends the user message to the FastAPI backend.""" - payload = { - "message": message, - "top_k": top_k, - "explain": explain - } + payload = {"message": message, "top_k": top_k, "explain": explain} try: - # Use the configured timeout from .env response = requests.post(CHAT_ENDPOINT, json=payload, timeout=API_TIMEOUT) response.raise_for_status() return response.json() except requests.exceptions.ReadTimeout: - return {"error": f"Timeout: Das Backend hat nicht innerhalb von {int(API_TIMEOUT)} Sekunden geantwortet. (Local LLM is busy)."} - except requests.exceptions.ConnectionError: - return {"error": f"Backend nicht erreichbar unter {API_BASE_URL}. Läuft der Server?"} + return {"error": f"Timeout ({int(API_TIMEOUT)}s). Das lokale LLM rechnet noch."} except Exception as e: return {"error": str(e)} -def send_feedback(query_id: str, score: int): - """Sends feedback to the backend.""" - # Note: We rate the overall answer. API expects node_id. - # We use 'generated_answer' as a convention for the full response. +def submit_feedback(query_id: str, node_id: str, score: int, comment: str = None): + """Sendet Feedback asynchron.""" payload = { "query_id": query_id, - "node_id": "generated_answer", + "node_id": node_id, "score": score, - "comment": "User feedback via Streamlit UI" + "comment": comment } try: requests.post(FEEDBACK_ENDPOINT, json=payload, timeout=5) - return True - except: - return False + # Wir nutzen st.toast für dezentes Feedback ohne Rerun + target = "Antwort" if node_id == "generated_answer" else "Quelle" + st.toast(f"Feedback für {target} gespeichert! (Score: {score})") + except Exception as e: + st.error(f"Feedback-Fehler: {e}") # --- UI COMPONENTS --- def render_sidebar(): with st.sidebar: st.header("⚙️ Konfiguration") - st.markdown(f"**Backend:** `{API_BASE_URL}`") - st.caption(f"⏱️ Timeout: {int(API_TIMEOUT)}s") + st.caption(f"Backend: `{API_BASE_URL}`") - st.markdown("---") - st.subheader("Retrieval Settings") - top_k = st.slider("Quellen (Top-K)", min_value=1, max_value=10, value=5) - explain_mode = st.checkbox("Explanation Layer", value=True, help="Zeigt an, warum Quellen gewählt wurden.") + st.subheader("Retrieval") + top_k = st.slider("Quellen Anzahl", 1, 10, 5) + explain_mode = st.toggle("Explanation Layer", value=True) - st.markdown("---") - st.markdown("### 🧠 System Status") - st.info(f"**Version:** v2.3.1\n\n**Modules:**\n- Decision Engine: ✅\n- Hybrid Router: ✅\n- Feedback Loop: ✅") - - if st.button("Clear Chat History"): + st.divider() + st.info("WP-10: Advanced Feedback Loop Active") + if st.button("Reset Chat"): st.session_state.messages = [] st.rerun() - return top_k, explain_mode def render_intent_badge(intent, source): - """Visualizes the Decision Engine state.""" icon = "🧠" if intent == "EMPATHY": icon = "❤️" elif intent == "DECISION": icon = "⚖️" elif intent == "CODING": icon = "💻" elif intent == "FACT": icon = "📚" - - return f""" -
- {icon} Intent: {intent} ({source}) -
- """ + return f"""
{icon} Intent: {intent} ({source})
""" -def render_sources(sources): - """Renders the retrieved sources in expandable cards.""" +def render_sources(sources, query_id): + """ + Rendert Quellen inklusive granularem Feedback-Mechanismus. + """ if not sources: return st.markdown("#### 📚 Verwendete Quellen") + for idx, hit in enumerate(sources): score = hit.get('total_score', 0) + node_id = hit.get('node_id') + title = hit.get('note_id', 'Unbekannt') payload = hit.get('payload', {}) note_type = payload.get('type', 'unknown') - title = hit.get('note_id', 'Unbekannt') - # Determine Header Color/Icon based on score + # Icon basierend auf Score score_icon = "🟢" if score > 0.8 else "🟡" if score > 0.5 else "⚪" + expander_title = f"{score_icon} {title} (Typ: {note_type}, Score: {score:.2f})" - with st.expander(f"{score_icon} {title} (Typ: {note_type}, Score: {score:.2f})"): - # Content - content = hit.get('source', {}).get('text', 'Kein Text verfügbar.') - st.markdown(f"_{content[:300]}..._") + with st.expander(expander_title): + # 1. Inhalt + text = hit.get('source', {}).get('text', 'Kein Text') + st.markdown(f"_{text[:300]}..._") - # Explanation (WP-04b) - explanation = hit.get('explanation') - if explanation: - st.markdown("---") - st.caption("**Warum wurde das gefunden?**") - reasons = explanation.get('reasons', []) - for r in reasons: + # 2. Explanation (Why-Layer) + if 'explanation' in hit and hit['explanation']: + st.caption("**Warum gefunden?**") + for r in hit['explanation'].get('reasons', []): st.caption(f"- {r.get('message')}") -# --- MAIN APP LOGIC --- + # 3. Granulares Feedback (Source Level) + st.markdown("---") + c1, c2 = st.columns([3, 1]) + with c1: + st.caption("War diese Quelle hilfreich für die Antwort?") + with c2: + # Callback Wrapper für Source-Feedback + def on_source_fb(qid=query_id, nid=node_id, k=f"fb_src_{node_id}"): + val = st.session_state.get(k) + # Mapping: Thumbs Up (1) -> Score 5, Thumbs Down (0) -> Score 1 + mapped_score = 5 if val == 1 else 1 + submit_feedback(qid, nid, mapped_score, comment="Source Feedback via UI") -top_k_setting, explain_setting = render_sidebar() + st.feedback( + "thumbs", + key=f"fb_src_{query_id}_{node_id}", # Unique Key pro Query/Node + on_change=on_source_fb, + kwargs={"qid": query_id, "nid": node_id, "k": f"fb_src_{query_id}_{node_id}"} + ) +# --- MAIN APP --- + +top_k, show_explain = render_sidebar() st.title("mindnet v2.3.1") -st.caption("Lead Frontend Architect Edition | WP-10 Chat Interface") -# 1. Render History +# 1. Chat History Rendern for msg in st.session_state.messages: with st.chat_message(msg["role"]): if msg["role"] == "assistant": - # Render Meta-Data first + # Meta-Daten if "intent" in msg: st.markdown(render_intent_badge(msg["intent"], msg.get("intent_source", "?")), unsafe_allow_html=True) + # Antwort-Text st.markdown(msg["content"]) - # Render Sources + # Quellen (mit Feedback-Option, aber Status ist readonly für alte Nachrichten in Streamlit oft schwierig, + # daher rendern wir Feedback-Controls idealerweise nur für die letzte Nachricht oder speichern Status) + # In dieser Version rendern wir sie immer, Streamlit State managed das. if "sources" in msg: - render_sources(msg["sources"]) + render_sources(msg["sources"], msg["query_id"]) - # Render Latency info - if "latency_ms" in msg: - st.caption(f"⏱️ Antwortzeit: {msg['latency_ms']}ms | Query-ID: `{msg.get('query_id')}`") + # Globales Feedback (Sterne) + qid = msg["query_id"] + + def on_global_fb(q=qid, k=f"fb_glob_{qid}"): + val = st.session_state.get(k) # Liefert 0-4 + if val is not None: + submit_feedback(q, "generated_answer", val + 1, comment="Global Star Rating") + + st.caption("Wie gut war diese Antwort?") + st.feedback( + "stars", + key=f"fb_glob_{qid}", + on_change=on_global_fb + ) else: st.markdown(msg["content"]) -# 2. Handle User Input -if prompt := st.chat_input("Was beschäftigt dich?"): - # Add User Message +# 2. User Input +if prompt := st.chat_input("Deine Frage an das System..."): + # User Message anzeigen st.session_state.messages.append({"role": "user", "content": prompt}) with st.chat_message("user"): st.markdown(prompt) - # Generate Response + # API Call with st.chat_message("assistant"): - message_placeholder = st.empty() - status_placeholder = st.empty() + with st.spinner("Thinking..."): + resp = send_chat_message(prompt, top_k, show_explain) - with st.spinner("Thinking... (Decision Engine Active)"): - api_response = send_chat_message(prompt, top_k_setting, explain_setting) - - if "error" in api_response: - st.error(api_response["error"]) + if "error" in resp: + st.error(resp["error"]) else: - # Extract data - answer = api_response.get("answer", "") - intent = api_response.get("intent", "FACT") - source = api_response.get("intent_source", "Unknown") - query_id = api_response.get("query_id") - hits = api_response.get("sources", []) - latency = api_response.get("latency_ms", 0) + # Daten extrahieren + answer = resp.get("answer", "") + intent = resp.get("intent", "FACT") + source = resp.get("intent_source", "Unknown") + query_id = resp.get("query_id") + hits = resp.get("sources", []) + + # Sofort rendern (damit User nicht auf Rerun warten muss) + st.markdown(render_intent_badge(intent, source), unsafe_allow_html=True) + st.markdown(answer) + render_sources(hits, query_id) + + # Feedback Slot für die NEUE Nachricht vorbereiten + st.caption("Wie gut war diese Antwort?") + 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)) - # 1. Show Intent - status_placeholder.markdown(render_intent_badge(intent, source), unsafe_allow_html=True) - - # 2. Show Answer - message_placeholder.markdown(answer) - - # 3. Show Sources - render_sources(hits) - - # 4. Show Latency & Feedback UI - st.caption(f"⏱️ {latency}ms | ID: `{query_id}`") - - # Feedback Buttons - col1, col2, col3, col4 = st.columns([1,1,1,4]) - with col1: - if st.button("👍", key=f"up_{query_id}"): - send_feedback(query_id, 5) - st.toast("Feedback gesendet: Positiv!") - with col2: - if st.button("👎", key=f"down_{query_id}"): - send_feedback(query_id, 1) - st.toast("Feedback gesendet: Negativ.") - - # Save to history + # In History speichern st.session_state.messages.append({ "role": "assistant", "content": answer, "intent": intent, "intent_source": source, "sources": hits, - "query_id": query_id, - "latency_ms": latency + "query_id": query_id }) \ No newline at end of file diff --git a/app/models/dto.py b/app/models/dto.py index 85767f8..22e4cff 100644 --- a/app/models/dto.py +++ b/app/models/dto.py @@ -6,7 +6,7 @@ Zweck: WP-06 Update: Intent & Intent-Source in ChatResponse. Version: - 0.6.1 (WP-06: Decision Engine Transparency) + 0.6.2 (WP-06: Decision Engine Transparency, Erweiterung des Feeback Request) Stand: 2025-12-09 """ @@ -64,11 +64,14 @@ class QueryRequest(BaseModel): class FeedbackRequest(BaseModel): """ - User-Feedback zu einem spezifischen Treffer. + User-Feedback zu einem spezifischen Treffer oder der Gesamtantwort. """ query_id: str = Field(..., description="ID der ursprünglichen Suche") - node_id: str = Field(..., description="ID des bewerteten Treffers") - score: int = Field(..., ge=0, le=1, description="1 (Positiv) oder 0 (Negativ/Irrelevant)") + # node_id ist optional: Wenn leer oder "generated_answer", gilt es für die Antwort. + # Wenn eine echte Chunk-ID, gilt es für die Quelle. + node_id: str = Field(..., description="ID des bewerteten Treffers oder 'generated_answer'") + # Update: Range auf 1-5 erweitert für differenziertes Tuning + score: int = Field(..., ge=1, le=5, description="1 (Irrelevant/Falsch) bis 5 (Perfekt)") comment: Optional[str] = None @@ -152,4 +155,6 @@ class ChatResponse(BaseModel): sources: List[QueryHit] = Field(..., description="Die für die Antwort genutzten Quellen") latency_ms: int intent: Optional[str] = Field("FACT", description="WP-06: Erkannter Intent (FACT/DECISION)") - intent_source: Optional[str] = Field("Unknown", description="WP-06: Quelle der Intent-Erkennung (Keyword vs. LLM)") \ No newline at end of file + intent_source: Optional[str] = Field("Unknown", description="WP-06: Quelle der Intent-Erkennung (Keyword vs. LLM)") + + \ No newline at end of file