feat: enhance AIPlanner with loading spinner during Ollama requests and update print statements for clarity

ai_planner.py

@@ -15,7 +15,9 @@ LLM 백엔드: 로컬 Ollama (structured output으로 JSON 스키마 강제)
 import json
 import os
 import re
+import sys
 import time
+import threading
 import traceback
 import ollama
 
@@ -141,7 +143,7 @@ class AIPlanner:
             "⚠️ 제작은 재료가 있어야 합니다. 인벤토리를 확인하세요."
         )
 
-        print(f"\n[AI] 생각 중... (model={OLLAMA_MODEL}, host={OLLAMA_HOST})")
+        print(f"\n[AI] 요청 시작 (model={OLLAMA_MODEL})")
 
         try:
             plan = self._call_ollama(user_message)
@@ -177,36 +179,53 @@ class AIPlanner:
 
     def _call_ollama(self, user_message: str) -> dict:
         t0 = time.perf_counter()
-        client = ollama.Client(host=OLLAMA_HOST)
+        stop_event = threading.Event()
-        response = client.chat(
+
-            model=OLLAMA_MODEL,
+        def _spinner():
-            messages=[
+            while not stop_event.is_set():
-                {"role": "system", "content": SYSTEM_PROMPT},
+                elapsed = time.perf_counter() - t0
-                {"role": "user",   "content": user_message},
+                print(f"\r[AI] 생각 중... {elapsed:.0f}s", end="", flush=True)
-            ],
+                stop_event.wait(1.0)
-            format={
+            print("\r", end="")
-                "type": "object",
+
-                "properties": {
+        spinner = threading.Thread(target=_spinner, daemon=True)
-                    "thinking":     {"type": "string"},
+        spinner.start()
-                    "current_goal": {"type": "string"},
+
-                    "actions": {
+        try:
-                        "type": "array",
+            client = ollama.Client(host=OLLAMA_HOST)
-                        "items": {
+            response = client.chat(
-                            "type": "object",
+                model=OLLAMA_MODEL,
-                            "properties": {
+                messages=[
-                                "action": {"type": "string"},
+                    {"role": "system", "content": SYSTEM_PROMPT},
-                                "params": {"type": "object"},
+                    {"role": "user",   "content": user_message},
-                                "reason": {"type": "string"},
+                ],
+                format={
+                    "type": "object",
+                    "properties": {
+                        "thinking":     {"type": "string"},
+                        "current_goal": {"type": "string"},
+                        "actions": {
+                            "type": "array",
+                            "items": {
+                                "type": "object",
+                                "properties": {
+                                    "action": {"type": "string"},
+                                    "params": {"type": "object"},
+                                    "reason": {"type": "string"},
+                                },
+                                "required": ["action", "params"],
                             },
-                            "required": ["action", "params"],
                         },
+                        "after_this": {"type": "string"},
                     },
-                    "after_this": {"type": "string"},
+                    "required": ["actions"],
                 },
-                "required": ["actions"],
+                options={"temperature": 0.3},
-            },
+            )
-            options={"temperature": 0.3},
+        finally:
-        )
+            stop_event.set()
+            spinner.join()
+
         dt = time.perf_counter() - t0
         content = response.message.content
         print(f"[AI] 응답 수신 ({dt:.2f}s, {len(content)}자)")

main.py

@@ -15,7 +15,7 @@ import json
 from factorio_rcon         import FactorioRCON
 from state_reader          import StateReader
 from context_compressor    import ContextCompressor
-from ai_planner            import AIPlanner
+from ai_planner            import AIPlanner, OLLAMA_MODEL, OLLAMA_HOST
 from action_executor       import ActionExecutor
 from agent_last_action_memory import save_last_action_memory, load_last_action_memory
 from ore_patch_memory import load_ore_patch_memory, compute_distance_sq
@@ -137,8 +137,8 @@ def run():
     print("=" * 60)
     print("  팩토리오 순수 AI 에이전트 (치트 없음)")
     print("  - 실제 걷기 / 실제 채굴 / 실제 제작 / 건설 거리 제한")
-    print(f"  - LLM: Ollama {os.environ.get('OLLAMA_MODEL', 'qwen3:14b')}")
+    print(f"  - LLM: Ollama {OLLAMA_MODEL}")
-    print(f"  - Ollama host: {os.environ.get('OLLAMA_HOST', 'http://192.168.50.67:11434')}")
+    print(f"  - Ollama host: {OLLAMA_HOST}")
     print("=" * 60)
 
     with FactorioRCON(RCON_HOST, RCON_PORT, RCON_PASSWORD) as rcon: