Erste Version Wp10

app/frontend/ui.py

@@ -0,0 +1,239 @@
+import streamlit as st
+import requests
+import uuid
+import os
+import json
+from datetime import datetime
+
+# --- CONFIGURATION ---
+# Default configuration taken from environment or fallback to localhost
+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"
+
+# --- PAGE SETUP ---
+st.set_page_config(
+    page_title="mindnet v2.3.1",
+    page_icon="🧠",
+    layout="centered"
+)
+
+# Custom CSS for cleaner look
+st.markdown("""
+<style>
+    .reportview-container { margin-top: -2em; }
+    .stDeployButton {display:none;}
+    .stMainMenu {visibility: hidden;}
+    div[data-testid="stExpander"] div[role="button"] p {
+        font-size: 0.9rem;
+        font-weight: 600;
+    }
+    .intent-badge {
+        background-color: #f0f2f6;
+        border-radius: 5px;
+        padding: 4px 8px;
+        font-size: 0.8em;
+        color: #555;
+        margin-bottom: 10px;
+        display: inline-block;
+        border: 1px solid #e0e0e0;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# --- SESSION STATE INITIALIZATION ---
+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 ---
+
+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
+    }
+    try:
+        response = requests.post(CHAT_ENDPOINT, json=payload, timeout=60)
+        response.raise_for_status()
+        return response.json()
+    except requests.exceptions.ConnectionError:
+        return {"error": "Backend nicht erreichbar. Läuft der Server auf Port 8002?"}
+    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.
+    payload = {
+        "query_id": query_id,
+        "node_id": "generated_answer", 
+        "score": score,
+        "comment": "User feedback via Streamlit UI"
+    }
+    try:
+        requests.post(FEEDBACK_ENDPOINT, json=payload, timeout=5)
+        return True
+    except:
+        return False
+
+# --- UI COMPONENTS ---
+
+def render_sidebar():
+    with st.sidebar:
+        st.header("⚙️ Konfiguration")
+        st.markdown(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.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.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"""
+    <div class="intent-badge">
+        {icon} <b>Intent:</b> {intent} <span style="color:#999">({source})</span>
+    </div>
+    """
+
+def render_sources(sources):
+    """Renders the retrieved sources in expandable cards."""
+    if not sources:
+        return
+    
+    st.markdown("#### 📚 Verwendete Quellen")
+    for idx, hit in enumerate(sources):
+        score = hit.get('total_score', 0)
+        payload = hit.get('payload', {})
+        note_type = payload.get('type', 'unknown')
+        title = hit.get('note_id', 'Unbekannt')
+        
+        # Determine Header Color/Icon based on score
+        score_icon = "🟢" if score > 0.8 else "🟡" if score > 0.5 else "⚪"
+        
+        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]}..._")
+            
+            # 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:
+                    st.caption(f"- {r.get('message')}")
+
+# --- MAIN APP LOGIC ---
+
+top_k_setting, explain_setting = render_sidebar()
+
+st.title("mindnet v2.3.1")
+st.caption("Lead Frontend Architect Edition | WP-10 Chat Interface")
+
+# 1. Render History
+for msg in st.session_state.messages:
+    with st.chat_message(msg["role"]):
+        if msg["role"] == "assistant":
+            # Render Meta-Data first
+            if "intent" in msg:
+                st.markdown(render_intent_badge(msg["intent"], msg.get("intent_source", "?")), unsafe_allow_html=True)
+            
+            st.markdown(msg["content"])
+            
+            # Render Sources
+            if "sources" in msg:
+                render_sources(msg["sources"])
+            
+            # Render Latency info
+            if "latency_ms" in msg:
+                st.caption(f"⏱️ Antwortzeit: {msg['latency_ms']}ms | Query-ID: `{msg.get('query_id')}`")
+                
+            # Render Feedback Controls (Static for history items to prevent re-run issues)
+            # (Note: In a prod app, we would check if feedback was already given)
+            
+        else:
+            st.markdown(msg["content"])
+
+# 2. Handle User Input
+if prompt := st.chat_input("Was beschäftigt dich?"):
+    # Add User Message
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    # Generate Response
+    with st.chat_message("assistant"):
+        message_placeholder = st.empty()
+        # Placeholder for intent badge
+        status_placeholder = st.empty()
+        
+        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"])
+        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)
+
+            # 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 (Directly here for the *new* message)
+            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
+            st.session_state.messages.append({
+                "role": "assistant",
+                "content": answer,
+                "intent": intent,
+                "intent_source": source,
+                "sources": hits,
+                "query_id": query_id,
+                "latency_ms": latency
+            })

requirements.txt

@@ -11,25 +11,23 @@ qdrant-client>=1.15.1
 pydantic>=2.11.7
 numpy>=2.3.2
 
-# --- Markdown & Parsing (Hier fehlten Pakete!) ---
+# --- Markdown & Parsing ---
 python-frontmatter>=1.1.0
-# WICHTIG: Das fehlte und verursachte den Fehler
 markdown-it-py>=3.0.0
-# WICHTIG: Für types.yaml und retriever.yaml
 PyYAML>=6.0.2
 python-slugify>=8.0.4
 
 # --- KI & Embeddings ---
 sentence-transformers>=5.1.0
-# Torch wird meist durch sentence-transformers geholt, 
-# aber wir listen es explizit für Stabilität
 torch>=2.0.0
 
 # --- Utilities ---
-# WICHTIG: Damit .env Dateien gelesen werden
 python-dotenv>=1.1.1
 requests>=2.32.5
 tqdm>=4.67.1
 
 # --- Testing ---
 pytest>=8.4.2
+
+# --- Frontend (WP-10) ---
+streamlit>=1.39.0