vorbereitung UI für WP07

app/frontend/ui.py

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