Nutzung von .env für Timeout

app/frontend/ui.py

@@ -4,13 +4,23 @@ import uuid
 import os
 import json
 from datetime import datetime
+from dotenv import load_dotenv
 
 # --- CONFIGURATION ---
-# Default configuration taken from environment or fallback to localhost
+# 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_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(
     page_title="mindnet v2.3.1",
@@ -57,11 +67,14 @@ def send_chat_message(message: str, top_k: int, explain: bool):
         "explain": explain
     }
     try:
-        response = requests.post(CHAT_ENDPOINT, json=payload, timeout=60)
+        # 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": "Backend nicht erreichbar. Läuft der Server auf Port 8002?"}
+        return {"error": f"Backend nicht erreichbar unter {API_BASE_URL}. Läuft der Server?"}
     except Exception as e:
         return {"error": str(e)}
 
@@ -87,6 +100,7 @@ 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.markdown("---")
         st.subheader("Retrieval Settings")
@@ -171,9 +185,6 @@ for msg in st.session_state.messages:
             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"])
 
@@ -187,7 +198,6 @@ if prompt := st.chat_input("Was beschäftigt dich?"):
     # 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)"):
@@ -216,7 +226,7 @@ if prompt := st.chat_input("Was beschäftigt dich?"):
             # 4. Show Latency & Feedback UI
             st.caption(f"⏱️ {latency}ms | ID: `{query_id}`")
             
-            # Feedback Buttons (Directly here for the *new* message)
+            # Feedback Buttons
             col1, col2, col3, col4 = st.columns([1,1,1,4])
             with col1:
                 if st.button("👍", key=f"up_{query_id}"):