refactor to reorganise codebase

build.py

@@ -158,7 +158,7 @@ def make_dmg(app_path: Path, dmg_path: Path, volume_name: str) -> None:
 def main() -> None:
     parser = argparse.ArgumentParser()
     parser.add_argument("--name", default="ChatMock")
-    parser.add_argument("--entry", default="app_qt.py")
+    parser.add_argument("--entry", default="gui.py")
     parser.add_argument("--icon", default="icon.png")
     parser.add_argument("--radius", type=float, default=0.22)
     parser.add_argument("--square", action="store_true")

chatmock/__init__.py

@@ -0,0 +1,5 @@
+from __future__ import annotations
+
+from .app import create_app
+from .cli import main
+

chatmock/app.py

@@ -0,0 +1,44 @@
+from __future__ import annotations
+
+from flask import Flask, jsonify
+
+from .config import BASE_INSTRUCTIONS
+from .http import build_cors_headers
+from .routes_openai import openai_bp
+from .routes_ollama import ollama_bp
+
+
+def create_app(
+    verbose: bool = False,
+    reasoning_effort: str = "medium",
+    reasoning_summary: str = "auto",
+    reasoning_compat: str = "think-tags",
+    debug_model: str | None = None,
+) -> Flask:
+    app = Flask(__name__)
+
+    app.config.update(
+        VERBOSE=bool(verbose),
+        REASONING_EFFORT=reasoning_effort,
+        REASONING_SUMMARY=reasoning_summary,
+        REASONING_COMPAT=reasoning_compat,
+        DEBUG_MODEL=debug_model,
+        BASE_INSTRUCTIONS=BASE_INSTRUCTIONS,
+    )
+
+    @app.get("/")
+    @app.get("/health")
+    def health():
+        return jsonify({"status": "ok"})
+
+    @app.after_request
+    def _cors(resp):
+        for k, v in build_cors_headers().items():
+            resp.headers.setdefault(k, v)
+        return resp
+
+    app.register_blueprint(openai_bp)
+    app.register_blueprint(ollama_bp)
+
+    return app
+

chatmock/cli.py

@@ -0,0 +1,165 @@
+from __future__ import annotations
+
+import argparse
+import json
+import errno
+import os
+import sys
+import webbrowser
+
+from .app import create_app
+from .config import CLIENT_ID_DEFAULT
+from .oauth import OAuthHTTPServer, OAuthHandler, REQUIRED_PORT, URL_BASE
+from .utils import eprint, get_home_dir, load_chatgpt_tokens, parse_jwt_claims, read_auth_file
+
+
+def cmd_login(no_browser: bool, verbose: bool) -> int:
+    home_dir = get_home_dir()
+    client_id = CLIENT_ID_DEFAULT
+    if not client_id:
+        eprint("ERROR: No OAuth client id configured. Set CHATGPT_LOCAL_CLIENT_ID.")
+        return 1
+
+    try:
+        httpd = OAuthHTTPServer(("127.0.0.1", REQUIRED_PORT), OAuthHandler, home_dir=home_dir, client_id=client_id, verbose=verbose)
+    except OSError as e:
+        eprint(f"ERROR: {e}")
+        if e.errno == errno.EADDRINUSE:
+            return 13
+        return 1
+
+    auth_url = httpd.auth_url()
+    with httpd:
+        eprint(f"Starting local login server on {URL_BASE}")
+        if not no_browser:
+            try:
+                webbrowser.open(auth_url, new=1, autoraise=True)
+            except Exception as e:
+                eprint(f"Failed to open browser: {e}")
+        eprint(f"If your browser did not open, navigate to:\n{auth_url}")
+        try:
+            httpd.serve_forever()
+        except KeyboardInterrupt:
+            eprint("\nKeyboard interrupt received, exiting.")
+        return httpd.exit_code
+
+
+def cmd_serve(
+    host: str,
+    port: int,
+    verbose: bool,
+    reasoning_effort: str,
+    reasoning_summary: str,
+    reasoning_compat: str,
+    debug_model: str | None,
+) -> int:
+    app = create_app(
+        verbose=verbose,
+        reasoning_effort=reasoning_effort,
+        reasoning_summary=reasoning_summary,
+        reasoning_compat=reasoning_compat,
+        debug_model=debug_model,
+    )
+
+    app.run(host=host, debug=False, use_reloader=False, port=port, threaded=True)
+    return 0
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="ChatGPT Local: login & OpenAI-compatible proxy")
+    sub = parser.add_subparsers(dest="command", required=True)
+
+    p_login = sub.add_parser("login", help="Authorize with ChatGPT and store tokens")
+    p_login.add_argument("--no-browser", action="store_true", help="Do not open the browser automatically")
+    p_login.add_argument("--verbose", action="store_true", help="Enable verbose logging")
+
+    p_serve = sub.add_parser("serve", help="Run local OpenAI-compatible server")
+    p_serve.add_argument("--host", default="127.0.0.1")
+    p_serve.add_argument("--port", type=int, default=8000)
+    p_serve.add_argument("--verbose", action="store_true", help="Enable verbose logging")
+    p_serve.add_argument(
+        "--debug-model",
+        dest="debug_model",
+        default=os.getenv("CHATGPT_LOCAL_DEBUG_MODEL"),
+        help="Forcibly override requested 'model' with this value",
+    )
+    p_serve.add_argument(
+        "--reasoning-effort",
+        choices=["low", "medium", "high", "none"],
+        default=os.getenv("CHATGPT_LOCAL_REASONING_EFFORT", "medium").lower(),
+        help="Reasoning effort level for Responses API (default: medium)",
+    )
+    p_serve.add_argument(
+        "--reasoning-summary",
+        choices=["auto", "concise", "detailed", "none"],
+        default=os.getenv("CHATGPT_LOCAL_REASONING_SUMMARY", "auto").lower(),
+        help="Reasoning summary verbosity (default: auto)",
+    )
+    p_serve.add_argument(
+        "--reasoning-compat",
+        choices=["legacy", "o3", "think-tags", "current"],
+        default=os.getenv("CHATGPT_LOCAL_REASONING_COMPAT", "think-tags").lower(),
+        help=(
+            "Compatibility mode for exposing reasoning to clients (legacy|o3|think-tags). "
+            "'current' is accepted as an alias for 'legacy'"
+        ),
+    )
+
+    p_info = sub.add_parser("info", help="Print current stored tokens and derived account id")
+    p_info.add_argument("--json", action="store_true", help="Output raw auth.json contents")
+
+    args = parser.parse_args()
+
+    if args.command == "login":
+        sys.exit(cmd_login(no_browser=args.no_browser, verbose=args.verbose))
+    elif args.command == "serve":
+        sys.exit(
+            cmd_serve(
+                host=args.host,
+                port=args.port,
+                verbose=args.verbose,
+                reasoning_effort=args.reasoning_effort,
+                reasoning_summary=args.reasoning_summary,
+                reasoning_compat=args.reasoning_compat,
+                debug_model=args.debug_model,
+            )
+        )
+    elif args.command == "info":
+        auth = read_auth_file()
+        if getattr(args, "json", False):
+            print(json.dumps(auth or {}, indent=2))
+            sys.exit(0)
+        access_token, account_id, id_token = load_chatgpt_tokens()
+        if not access_token or not id_token:
+            print("👤 Account")
+            print("  • Not signed in")
+            print("  • Run: python3 chatmock.py login")
+            sys.exit(0)
+
+        id_claims = parse_jwt_claims(id_token) or {}
+        access_claims = parse_jwt_claims(access_token) or {}
+
+        email = id_claims.get("email") or id_claims.get("preferred_username") or "<unknown>"
+        plan_raw = (access_claims.get("https://api.openai.com/auth") or {}).get("chatgpt_plan_type") or "unknown"
+        plan_map = {
+            "plus": "Plus",
+            "pro": "Pro",
+            "free": "Free",
+            "team": "Team",
+            "enterprise": "Enterprise",
+        }
+        plan = plan_map.get(str(plan_raw).lower(), str(plan_raw).title() if isinstance(plan_raw, str) else "Unknown")
+
+        print("👤 Account")
+        print("  • Signed in with ChatGPT")
+        print(f"  • Login: {email}")
+        print(f"  • Plan: {plan}")
+        if account_id:
+            print(f"  • Account ID: {account_id}")
+        sys.exit(0)
+    else:
+        parser.error("Unknown command")
+
+
+if __name__ == "__main__":
+    main()

chatmock/config.py

@@ -0,0 +1,35 @@
+from __future__ import annotations
+
+import os
+import sys
+from pathlib import Path
+
+
+CLIENT_ID_DEFAULT = os.getenv("CHATGPT_LOCAL_CLIENT_ID") or "app_EMoamEEZ73f0CkXaXp7hrann"
+
+CHATGPT_RESPONSES_URL = "https://chatgpt.com/backend-api/codex/responses"
+
+
+def read_base_instructions() -> str:
+    candidates = [
+        Path(__file__).parent.parent / "prompt.md",
+        Path(__file__).parent / "prompt.md",
+        Path(getattr(sys, "_MEIPASS", "")) / "prompt.md" if getattr(sys, "_MEIPASS", None) else None,
+        Path.cwd() / "prompt.md",
+    ]
+    for p in candidates:
+        if not p:
+            continue
+        try:
+            if p.exists():
+                content = p.read_text(encoding="utf-8")
+                if isinstance(content, str) and content.strip():
+                    return content
+        except Exception:
+            continue
+    raise FileNotFoundError(
+        "Failed to read prompt.md; expected adjacent to package or CWD."
+    )
+
+
+BASE_INSTRUCTIONS = read_base_instructions()

chatmock/http.py

@@ -0,0 +1,24 @@
+from __future__ import annotations
+
+from flask import Response, jsonify, request
+
+
+def build_cors_headers() -> dict:
+    origin = request.headers.get("Origin", "*")
+    req_headers = request.headers.get("Access-Control-Request-Headers")
+    allow_headers = req_headers if req_headers else "Authorization, Content-Type, Accept"
+    return {
+        "Access-Control-Allow-Origin": origin,
+        "Access-Control-Allow-Methods": "POST, GET, OPTIONS",
+        "Access-Control-Allow-Headers": allow_headers,
+        "Access-Control-Max-Age": "86400",
+    }
+
+
+def json_error(message: str, status: int = 400) -> Response:
+    resp = jsonify({"error": {"message": message}})
+    response: Response = Response(response=resp.response, status=status, mimetype="application/json")
+    for k, v in build_cors_headers().items():
+        response.headers.setdefault(k, v)
+    return response
+

chatmock/models.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from dataclasses import dataclass
 from typing import Optional
 

chatmock/oauth.py

@@ -10,8 +10,8 @@ import urllib.parse
 import urllib.request
 from typing import Any, Dict, Tuple
 
-from models import AuthBundle, PkceCodes, TokenData
-from utils import eprint, generate_pkce, parse_jwt_claims, write_auth_file
+from .models import AuthBundle, PkceCodes, TokenData
+from .utils import eprint, generate_pkce, parse_jwt_claims, write_auth_file
 
 
 REQUIRED_PORT = 1455
@@ -258,3 +258,4 @@ class OAuthHandler(http.server.BaseHTTPRequestHandler):
         }
         success_url = f"{URL_BASE}/success?{urllib.parse.urlencode(success_url_query)}"
         return exchanged_access_token, success_url
+

chatmock/reasoning.py

@@ -0,0 +1,74 @@
+from __future__ import annotations
+
+from typing import Any, Dict
+
+
+def build_reasoning_param(
+    base_effort: str = "medium", base_summary: str = "auto", overrides: Dict[str, Any] | None = None
+) -> Dict[str, Any]:
+    effort = (base_effort or "").strip().lower()
+    summary = (base_summary or "").strip().lower()
+
+    valid_efforts = {"low", "medium", "high", "none"}
+    valid_summaries = {"auto", "concise", "detailed", "none"}
+
+    if isinstance(overrides, dict):
+        o_eff = str(overrides.get("effort", "")).strip().lower()
+        o_sum = str(overrides.get("summary", "")).strip().lower()
+        if o_eff in valid_efforts and o_eff:
+            effort = o_eff
+        if o_sum in valid_summaries and o_sum:
+            summary = o_sum
+    if effort not in valid_efforts:
+        effort = "medium"
+    if summary not in valid_summaries:
+        summary = "auto"
+
+    reasoning: Dict[str, Any] = {"effort": effort}
+    if summary != "none":
+        reasoning["summary"] = summary
+    return reasoning
+
+
+def apply_reasoning_to_message(
+    message: Dict[str, Any],
+    reasoning_summary_text: str,
+    reasoning_full_text: str,
+    compat: str,
+) -> Dict[str, Any]:
+    try:
+        compat = (compat or "think-tags").strip().lower()
+    except Exception:
+        compat = "think-tags"
+
+    if compat == "o3":
+        rtxt_parts: list[str] = []
+        if isinstance(reasoning_summary_text, str) and reasoning_summary_text.strip():
+            rtxt_parts.append(reasoning_summary_text)
+        if isinstance(reasoning_full_text, str) and reasoning_full_text.strip():
+            rtxt_parts.append(reasoning_full_text)
+        rtxt = "\n\n".join([p for p in rtxt_parts if p])
+        if rtxt:
+            message["reasoning"] = {"content": [{"type": "text", "text": rtxt}]}
+        return message
+
+    if compat in ("legacy", "current"):
+        if reasoning_summary_text:
+            message["reasoning_summary"] = reasoning_summary_text
+        if reasoning_full_text:
+            message["reasoning"] = reasoning_full_text
+        return message
+
+    rtxt_parts: list[str] = []
+    if isinstance(reasoning_summary_text, str) and reasoning_summary_text.strip():
+        rtxt_parts.append(reasoning_summary_text)
+    if isinstance(reasoning_full_text, str) and reasoning_full_text.strip():
+        rtxt_parts.append(reasoning_full_text)
+    rtxt = "\n\n".join([p for p in rtxt_parts if p])
+    if rtxt:
+        think_block = f"<think>{rtxt}</think>"
+        content_text = message.get("content") or ""
+        if isinstance(content_text, str):
+            message["content"] = think_block + (content_text or "")
+    return message
+

chatmock/routes_ollama.py

@@ -0,0 +1,299 @@
+from __future__ import annotations
+
+import json
+import time
+from typing import Any, Dict, List
+
+from flask import Blueprint, Response, current_app, jsonify, make_response, request
+
+from .config import BASE_INSTRUCTIONS
+from .http import build_cors_headers
+from .reasoning import build_reasoning_param
+from .transform import convert_ollama_messages, normalize_ollama_tools
+from .upstream import normalize_model_name, start_upstream_request
+from .utils import convert_chat_messages_to_responses_input, convert_tools_chat_to_responses
+
+
+ollama_bp = Blueprint("ollama", __name__)
+
+
+_OLLAMA_FAKE_EVAL = {
+    "total_duration": 8497226791,
+    "load_duration": 1747193958,
+    "prompt_eval_count": 24,
+    "prompt_eval_duration": 269219750,
+    "eval_count": 247,
+    "eval_duration": 6413802458,
+}
+
+
+@ollama_bp.route("/api/tags", methods=["GET"])
+def ollama_tags() -> Response:
+    if bool(current_app.config.get("VERBOSE")):
+        print("IN GET /api/tags")
+    model_id = "gpt-5"
+    models = [
+        {
+            "name": model_id,
+            "model": model_id,
+            "modified_at": "2023-10-01T00:00:00Z",
+            "size": 815319791,
+            "digest": "8648f39daa8fbf5b18c7b4e6a8fb4990c692751d49917417b8842ca5758e7ffc",
+            "details": {
+                "parent_model": "",
+                "format": "gguf",
+                "family": "llama",
+                "families": ["llama"],
+                "parameter_size": "8.0B",
+                "quantization_level": "Q4_0",
+            },
+        }
+    ]
+    resp = make_response(jsonify({"models": models}), 200)
+    for k, v in build_cors_headers().items():
+        resp.headers.setdefault(k, v)
+    return resp
+
+
+@ollama_bp.route("/api/show", methods=["POST"])
+def ollama_show() -> Response:
+    verbose = bool(current_app.config.get("VERBOSE"))
+    try:
+        if verbose:
+            body_preview = (request.get_data(cache=True, as_text=True) or "")[:2000]
+            print("IN POST /api/show\n" + body_preview)
+    except Exception:
+        pass
+    try:
+        payload = request.get_json(silent=True) or {}
+    except Exception:
+        payload = {}
+    model = payload.get("model")
+    if not isinstance(model, str) or not model.strip():
+        return jsonify({"error": "Model not found"}), 400
+    v1_show_response = {
+        "modelfile": "# Modelfile generated by \"ollama show\"\n# To build a new Modelfile based on this one, replace the FROM line with:\n# FROM llava:latest\n\nFROM /models/blobs/sha256:placeholder\nTEMPLATE \"\"\"{{ .System }}\nUSER: {{ .Prompt }}\nASSISTANT: \"\"\"\nPARAMETER num_ctx 100000\nPARAMETER stop \"</s>\"\nPARAMETER stop \"USER:\"\nPARAMETER stop \"ASSISTANT:\"",
+        "parameters": "num_keep 24\nstop \"<|start_header_id|>\"\nstop \"<|end_header_id|>\"\nstop \"<|eot_id|>\"",
+        "template": "{{ if .System }}<|start_header_id|>system<|end_header_id|>\n\n{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>\n\n{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>\n\n{{ .Response }}<|eot_id|>",
+        "details": {
+            "parent_model": "",
+            "format": "gguf",
+            "family": "llama",
+            "families": ["llama"],
+            "parameter_size": "8.0B",
+            "quantization_level": "Q4_0",
+        },
+        "model_info": {
+            "general.architecture": "llama",
+            "general.file_type": 2,
+            "llama.context_length": 2000000,
+        },
+        "capabilities": ["completion", "vision", "tools", "thinking"],
+    }
+    resp = make_response(jsonify(v1_show_response), 200)
+    for k, v in build_cors_headers().items():
+        resp.headers.setdefault(k, v)
+    return resp
+
+
+@ollama_bp.route("/api/chat", methods=["POST"])
+def ollama_chat() -> Response:
+    verbose = bool(current_app.config.get("VERBOSE"))
+    reasoning_effort = current_app.config.get("REASONING_EFFORT", "medium")
+    reasoning_summary = current_app.config.get("REASONING_SUMMARY", "auto")
+    reasoning_compat = current_app.config.get("REASONING_COMPAT", "think-tags")
+
+    try:
+        raw = request.get_data(cache=True, as_text=True) or ""
+        if verbose:
+            print("IN POST /api/chat\n" + (raw[:2000] if isinstance(raw, str) else ""))
+        payload = json.loads(raw) if raw else {}
+    except Exception:
+        return jsonify({"error": "Invalid JSON body"}), 400
+
+    model = payload.get("model")
+    raw_messages = payload.get("messages")
+    messages = convert_ollama_messages(
+        raw_messages, payload.get("images") if isinstance(payload.get("images"), list) else None
+    )
+    if isinstance(messages, list):
+        sys_idx = next((i for i, m in enumerate(messages) if isinstance(m, dict) and m.get("role") == "system"), None)
+        if isinstance(sys_idx, int):
+            sys_msg = messages.pop(sys_idx)
+            content = sys_msg.get("content") if isinstance(sys_msg, dict) else ""
+            messages.insert(0, {"role": "user", "content": content})
+    stream_req = payload.get("stream")
+    if stream_req is None:
+        stream_req = True
+    stream_req = bool(stream_req)
+    tools_req = payload.get("tools") if isinstance(payload.get("tools"), list) else []
+    tools_responses = convert_tools_chat_to_responses(normalize_ollama_tools(tools_req))
+    tool_choice = payload.get("tool_choice", "auto")
+    parallel_tool_calls = bool(payload.get("parallel_tool_calls", False))
+
+    if not isinstance(model, str) or not isinstance(messages, list) or not messages:
+        return jsonify({"error": "Invalid request format"}), 400
+
+    input_items = convert_chat_messages_to_responses_input(messages)
+
+    upstream, error_resp = start_upstream_request(
+        normalize_model_name(model),
+        input_items,
+        instructions=BASE_INSTRUCTIONS,
+        tools=tools_responses,
+        tool_choice=tool_choice,
+        parallel_tool_calls=parallel_tool_calls,
+        reasoning_param=build_reasoning_param(reasoning_effort, reasoning_summary, None),
+    )
+    if error_resp is not None:
+        return error_resp
+
+    if upstream.status_code >= 400:
+        try:
+            err_body = json.loads(upstream.content.decode("utf-8", errors="ignore")) if upstream.content else {"raw": upstream.text}
+        except Exception:
+            err_body = {"raw": upstream.text}
+        if verbose:
+            print("/api/chat upstream error status=", upstream.status_code, " body:", json.dumps(err_body)[:2000])
+        return (
+            jsonify({"error": (err_body.get("error", {}) or {}).get("message", "Upstream error")}),
+            upstream.status_code,
+        )
+
+    created_at = str(int(time.time() * 1000))
+
+    if stream_req:
+        def _gen():
+            compat = (current_app.config.get("REASONING_COMPAT", "think-tags") or "think-tags").strip().lower()
+            think_open = False
+            think_closed = False
+            saw_any_summary = False
+            pending_summary_paragraph = False
+            try:
+                for raw_line in upstream.iter_lines(decode_unicode=False):
+                    if not raw_line:
+                        continue
+                    line = raw_line.decode("utf-8", errors="ignore") if isinstance(raw_line, (bytes, bytearray)) else raw_line
+                    if not line.startswith("data: "):
+                        continue
+                    data = line[len("data: "):].strip()
+                    if not data:
+                        continue
+                    if data == "[DONE]":
+                        break
+                    try:
+                        evt = json.loads(data)
+                    except Exception:
+                        continue
+                    kind = evt.get("type")
+                    if kind == "response.reasoning_summary_part.added":
+                        if compat in ("think-tags", "o3"):
+                            if saw_any_summary:
+                                pending_summary_paragraph = True
+                            else:
+                                saw_any_summary = True
+                    elif kind in ("response.reasoning_summary_text.delta", "response.reasoning_text.delta"):
+                        delta_txt = evt.get("delta") or ""
+                        if compat == "o3":
+                            if kind == "response.reasoning_summary_text.delta" and pending_summary_paragraph:
+                                yield json.dumps({"message": {"role": "assistant", "content": "\n"}}) + "\n"
+                                pending_summary_paragraph = False
+                        elif compat == "think-tags":
+                            if not think_open and not think_closed:
+                                yield json.dumps({"message": {"role": "assistant", "content": "<think>"}}) + "\n"
+                                think_open = True
+                            if think_open and not think_closed:
+                                if kind == "response.reasoning_summary_text.delta" and pending_summary_paragraph:
+                                    yield json.dumps({"message": {"role": "assistant", "content": "\n"}}) + "\n"
+                                    pending_summary_paragraph = False
+                        else:
+                            pass
+                    elif kind == "response.output_text.delta":
+                        delta = evt.get("delta") or ""
+                        if compat == "think-tags" and think_open and not think_closed:
+                            yield json.dumps({"message": {"role": "assistant", "content": "</think>"}}) + "\n"
+                            think_open = False
+                            think_closed = True
+                        yield json.dumps({"message": {"role": "assistant", "content": delta}}) + "\n"
+                    elif kind == "response.completed":
+                        break
+            finally:
+                upstream.close()
+        resp = current_app.response_class(
+            _gen(),
+            status=200,
+            mimetype="application/x-ndjson",
+        )
+        for k, v in build_cors_headers().items():
+            resp.headers.setdefault(k, v)
+        return resp
+
+    full_text = ""
+    reasoning_summary_text = ""
+    reasoning_full_text = ""
+    tool_calls: List[Dict[str, Any]] = []
+    try:
+        for raw in upstream.iter_lines(decode_unicode=False):
+            if not raw:
+                continue
+            line = raw.decode("utf-8", errors="ignore") if isinstance(raw, (bytes, bytearray)) else raw
+            if not line.startswith("data: "):
+                continue
+            data = line[len("data: "):].strip()
+            if not data:
+                continue
+            if data == "[DONE]":
+                break
+            try:
+                evt = json.loads(data)
+            except Exception:
+                continue
+            kind = evt.get("type")
+            if kind == "response.output_text.delta":
+                full_text += evt.get("delta") or ""
+            elif kind == "response.reasoning_summary_text.delta":
+                reasoning_summary_text += evt.get("delta") or ""
+            elif kind == "response.reasoning_text.delta":
+                reasoning_full_text += evt.get("delta") or ""
+            elif kind == "response.output_item.done":
+                item = evt.get("item") or {}
+                if isinstance(item, dict) and item.get("type") == "function_call":
+                    call_id = item.get("call_id") or item.get("id") or ""
+                    name = item.get("name") or ""
+                    args = item.get("arguments") or ""
+                    if isinstance(call_id, str) and isinstance(name, str) and isinstance(args, str):
+                        tool_calls.append(
+                            {
+                                "id": call_id,
+                                "type": "function",
+                                "function": {"name": name, "arguments": args},
+                            }
+                        )
+            elif kind == "response.completed":
+                break
+    finally:
+        upstream.close()
+
+    if (current_app.config.get("REASONING_COMPAT", "think-tags") or "think-tags").strip().lower() == "think-tags":
+        rtxt_parts = []
+        if isinstance(reasoning_summary_text, str) and reasoning_summary_text.strip():
+            rtxt_parts.append(reasoning_summary_text)
+        if isinstance(reasoning_full_text, str) and reasoning_full_text.strip():
+            rtxt_parts.append(reasoning_full_text)
+        rtxt = "\n\n".join([p for p in rtxt_parts if p])
+        if rtxt:
+            full_text = f"<think>{rtxt}</think>" + (full_text or "")
+
+    out_json = {
+        "model": normalize_model_name(model),
+        "created_at": created_at,
+        "message": {"role": "assistant", "content": full_text, **({"tool_calls": tool_calls} if tool_calls else {})},
+        "done": True,
+        "done_reason": "stop",
+    }
+    out_json.update(_OLLAMA_FAKE_EVAL)
+    resp = make_response(jsonify(out_json), 200)
+    for k, v in build_cors_headers().items():
+        resp.headers.setdefault(k, v)
+    return resp
+

chatmock/routes_openai.py

@@ -0,0 +1,313 @@
+from __future__ import annotations
+
+import json
+import time
+from typing import Any, Dict, List
+
+from flask import Blueprint, Response, current_app, jsonify, make_response, request
+
+from .config import BASE_INSTRUCTIONS
+from .http import build_cors_headers
+from .reasoning import apply_reasoning_to_message, build_reasoning_param
+from .upstream import normalize_model_name, start_upstream_request
+from .utils import (
+    convert_chat_messages_to_responses_input,
+    convert_tools_chat_to_responses,
+    sse_translate_chat,
+    sse_translate_text,
+)
+
+
+openai_bp = Blueprint("openai", __name__)
+
+
+@openai_bp.route("/v1/chat/completions", methods=["POST"])
+def chat_completions() -> Response:
+    verbose = bool(current_app.config.get("VERBOSE"))
+    reasoning_effort = current_app.config.get("REASONING_EFFORT", "medium")
+    reasoning_summary = current_app.config.get("REASONING_SUMMARY", "auto")
+    reasoning_compat = current_app.config.get("REASONING_COMPAT", "think-tags")
+    debug_model = current_app.config.get("DEBUG_MODEL")
+
+    if verbose:
+        try:
+            body_preview = (request.get_data(cache=True, as_text=True) or "")[:2000]
+            print("IN POST /v1/chat/completions\n" + body_preview)
+        except Exception:
+            pass
+
+    raw = request.get_data(cache=True, as_text=True) or ""
+    try:
+        payload = json.loads(raw) if raw else {}
+    except Exception:
+        try:
+            payload = json.loads(raw.replace("\r", "").replace("\n", ""))
+        except Exception:
+            return jsonify({"error": {"message": "Invalid JSON body"}}), 400
+
+    model = normalize_model_name(payload.get("model"), debug_model)
+    messages = payload.get("messages")
+    if messages is None and isinstance(payload.get("prompt"), str):
+        messages = [{"role": "user", "content": payload.get("prompt") or ""}]
+    if messages is None and isinstance(payload.get("input"), str):
+        messages = [{"role": "user", "content": payload.get("input") or ""}]
+    if messages is None:
+        messages = []
+    if not isinstance(messages, list):
+        return jsonify({"error": {"message": "Request must include messages: []"}}), 400
+
+    if isinstance(messages, list):
+        sys_idx = next((i for i, m in enumerate(messages) if isinstance(m, dict) and m.get("role") == "system"), None)
+        if isinstance(sys_idx, int):
+            sys_msg = messages.pop(sys_idx)
+            content = sys_msg.get("content") if isinstance(sys_msg, dict) else ""
+            messages.insert(0, {"role": "user", "content": content})
+    is_stream = bool(payload.get("stream"))
+
+    tools_responses = convert_tools_chat_to_responses(payload.get("tools"))
+    tool_choice = payload.get("tool_choice", "auto")
+    parallel_tool_calls = bool(payload.get("parallel_tool_calls", False))
+
+    input_items = convert_chat_messages_to_responses_input(messages)
+    if not input_items and isinstance(payload.get("prompt"), str) and payload.get("prompt").strip():
+        input_items = [
+            {"type": "message", "role": "user", "content": [{"type": "input_text", "text": payload.get("prompt")}]}
+        ]
+
+    reasoning_overrides = payload.get("reasoning") if isinstance(payload.get("reasoning"), dict) else None
+    reasoning_param = build_reasoning_param(reasoning_effort, reasoning_summary, reasoning_overrides)
+
+    upstream, error_resp = start_upstream_request(
+        model,
+        input_items,
+        instructions=BASE_INSTRUCTIONS,
+        tools=tools_responses,
+        tool_choice=tool_choice,
+        parallel_tool_calls=parallel_tool_calls,
+        reasoning_param=reasoning_param,
+    )
+    if error_resp is not None:
+        return error_resp
+
+    created = int(time.time())
+    if upstream.status_code >= 400:
+        try:
+            raw = upstream.content
+            err_body = json.loads(raw.decode("utf-8", errors="ignore")) if raw else {"raw": upstream.text}
+        except Exception:
+            err_body = {"raw": upstream.text}
+        if verbose:
+            print("Upstream error status=", upstream.status_code, " body:", json.dumps(err_body)[:2000])
+        return (
+            jsonify({"error": {"message": (err_body.get("error", {}) or {}).get("message", "Upstream error")}}),
+            upstream.status_code,
+        )
+
+    if is_stream:
+        resp = Response(
+            sse_translate_chat(
+                upstream,
+                model,
+                created,
+                verbose=verbose,
+                vlog=print if verbose else None,
+                reasoning_compat=reasoning_compat,
+            ),
+            status=upstream.status_code,
+            mimetype="text/event-stream",
+            headers={"Cache-Control": "no-cache", "Connection": "keep-alive"},
+        )
+        for k, v in build_cors_headers().items():
+            resp.headers.setdefault(k, v)
+        return resp
+
+    full_text = ""
+    reasoning_summary_text = ""
+    reasoning_full_text = ""
+    response_id = "chatcmpl"
+    tool_calls: List[Dict[str, Any]] = []
+    error_message: str | None = None
+    try:
+        for raw in upstream.iter_lines(decode_unicode=False):
+            if not raw:
+                continue
+            line = raw.decode("utf-8", errors="ignore") if isinstance(raw, (bytes, bytearray)) else raw
+            if not line.startswith("data: "):
+                continue
+            data = line[len("data: "):].strip()
+            if not data:
+                continue
+            if data == "[DONE]":
+                break
+            try:
+                evt = json.loads(data)
+            except Exception:
+                continue
+            kind = evt.get("type")
+            if isinstance(evt.get("response"), dict) and isinstance(evt["response"].get("id"), str):
+                response_id = evt["response"].get("id") or response_id
+            if kind == "response.output_text.delta":
+                full_text += evt.get("delta") or ""
+            elif kind == "response.reasoning_summary_text.delta":
+                reasoning_summary_text += evt.get("delta") or ""
+            elif kind == "response.reasoning_text.delta":
+                reasoning_full_text += evt.get("delta") or ""
+            elif kind == "response.output_item.done":
+                item = evt.get("item") or {}
+                if isinstance(item, dict) and item.get("type") == "function_call":
+                    call_id = item.get("call_id") or item.get("id") or ""
+                    name = item.get("name") or ""
+                    args = item.get("arguments") or ""
+                    if isinstance(call_id, str) and isinstance(name, str) and isinstance(args, str):
+                        tool_calls.append(
+                            {
+                                "id": call_id,
+                                "type": "function",
+                                "function": {"name": name, "arguments": args},
+                            }
+                        )
+            elif kind == "response.failed":
+                error_message = evt.get("response", {}).get("error", {}).get("message", "response.failed")
+            elif kind == "response.completed":
+                break
+    finally:
+        upstream.close()
+
+    if error_message:
+        resp = make_response(jsonify({"error": {"message": error_message}}), 502)
+        for k, v in build_cors_headers().items():
+            resp.headers.setdefault(k, v)
+        return resp
+
+    message: Dict[str, Any] = {"role": "assistant", "content": full_text if full_text else None}
+    if tool_calls:
+        message["tool_calls"] = tool_calls
+    message = apply_reasoning_to_message(message, reasoning_summary_text, reasoning_full_text, reasoning_compat)
+
+    completion = {
+        "id": response_id or "chatcmpl",
+        "object": "chat.completion",
+        "created": created,
+        "model": model,
+        "choices": [
+            {
+                "index": 0,
+                "message": message,
+                "finish_reason": "stop",
+            }
+        ],
+    }
+    resp = make_response(jsonify(completion), upstream.status_code)
+    for k, v in build_cors_headers().items():
+        resp.headers.setdefault(k, v)
+    return resp
+
+
+@openai_bp.route("/v1/completions", methods=["POST"])
+def completions() -> Response:
+    verbose = bool(current_app.config.get("VERBOSE"))
+    debug_model = current_app.config.get("DEBUG_MODEL")
+    reasoning_effort = current_app.config.get("REASONING_EFFORT", "medium")
+    reasoning_summary = current_app.config.get("REASONING_SUMMARY", "auto")
+
+    raw = request.get_data(cache=True, as_text=True) or ""
+    try:
+        payload = json.loads(raw) if raw else {}
+    except Exception:
+        return jsonify({"error": {"message": "Invalid JSON body"}}), 400
+
+    model = normalize_model_name(payload.get("model"), debug_model)
+    prompt = payload.get("prompt")
+    if isinstance(prompt, list):
+        prompt = "".join([p if isinstance(p, str) else "" for p in prompt])
+    if not isinstance(prompt, str):
+        prompt = payload.get("suffix") or ""
+    stream_req = bool(payload.get("stream", False))
+
+    messages = [{"role": "user", "content": prompt or ""}]
+    input_items = convert_chat_messages_to_responses_input(messages)
+
+    reasoning_overrides = payload.get("reasoning") if isinstance(payload.get("reasoning"), dict) else None
+    reasoning_param = build_reasoning_param(reasoning_effort, reasoning_summary, reasoning_overrides)
+    upstream, error_resp = start_upstream_request(
+        model,
+        input_items,
+        instructions=BASE_INSTRUCTIONS,
+        reasoning_param=reasoning_param,
+    )
+    if error_resp is not None:
+        return error_resp
+
+    created = int(time.time())
+    if upstream.status_code >= 400:
+        try:
+            err_body = json.loads(upstream.content.decode("utf-8", errors="ignore")) if upstream.content else {"raw": upstream.text}
+        except Exception:
+            err_body = {"raw": upstream.text}
+        return (
+            jsonify({"error": {"message": (err_body.get("error", {}) or {}).get("message", "Upstream error")}}),
+            upstream.status_code,
+        )
+
+    if stream_req:
+        resp = Response(
+            sse_translate_text(upstream, model, created, verbose=verbose, vlog=(print if verbose else None)),
+            status=upstream.status_code,
+            mimetype="text/event-stream",
+            headers={"Cache-Control": "no-cache", "Connection": "keep-alive"},
+        )
+        for k, v in build_cors_headers().items():
+            resp.headers.setdefault(k, v)
+        return resp
+
+    full_text = ""
+    response_id = "cmpl"
+    try:
+        for raw_line in upstream.iter_lines(decode_unicode=False):
+            if not raw_line:
+                continue
+            line = raw_line.decode("utf-8", errors="ignore") if isinstance(raw_line, (bytes, bytearray)) else raw_line
+            if not line.startswith("data: "):
+                continue
+            data = line[len("data: "):].strip()
+            if not data or data == "[DONE]":
+                if data == "[DONE]":
+                    break
+                continue
+            try:
+                evt = json.loads(data)
+            except Exception:
+                continue
+            if isinstance(evt.get("response"), dict) and isinstance(evt["response"].get("id"), str):
+                response_id = evt["response"].get("id") or response_id
+            kind = evt.get("type")
+            if kind == "response.output_text.delta":
+                full_text += evt.get("delta") or ""
+            elif kind == "response.completed":
+                break
+    finally:
+        upstream.close()
+
+    completion = {
+        "id": response_id or "cmpl",
+        "object": "text_completion",
+        "created": created,
+        "model": model,
+        "choices": [
+            {"index": 0, "text": full_text, "finish_reason": "stop", "logprobs": None}
+        ],
+    }
+    resp = make_response(jsonify(completion), upstream.status_code)
+    for k, v in build_cors_headers().items():
+        resp.headers.setdefault(k, v)
+    return resp
+
+
+@openai_bp.route("/v1/models", methods=["GET"])
+def list_models() -> Response:
+    models = {"object": "list", "data": [{"id": "gpt-5", "object": "model", "owned_by": "owner"}]}
+    resp = make_response(jsonify(models), 200)
+    for k, v in build_cors_headers().items():
+        resp.headers.setdefault(k, v)
+    return resp
+

chatmock/transform.py

@@ -0,0 +1,149 @@
+from __future__ import annotations
+
+import json
+from typing import Any, Dict, List
+
+
+def to_data_url(image_str: str) -> str:
+    if not isinstance(image_str, str) or not image_str:
+        return image_str
+    s = image_str.strip()
+    if s.startswith("data:image/"):
+        return s
+    if s.startswith("http://") or s.startswith("https://"):
+        return s
+    b64 = s.replace("\n", "").replace("\r", "")
+    kind = "image/png"
+    if b64.startswith("/9j/"):
+        kind = "image/jpeg"
+    elif b64.startswith("iVBORw0KGgo"):
+        kind = "image/png"
+    elif b64.startswith("R0lGOD"):
+        kind = "image/gif"
+    return f"data:{kind};base64,{b64}"
+
+
+def convert_ollama_messages(
+    messages: List[Dict[str, Any]] | None, top_images: List[str] | None
+) -> List[Dict[str, Any]]:
+    out: List[Dict[str, Any]] = []
+    msgs = messages if isinstance(messages, list) else []
+    pending_call_ids: List[str] = []
+    call_counter = 0
+    for m in msgs:
+        if not isinstance(m, dict):
+            continue
+        role = m.get("role") or "user"
+        nm: Dict[str, Any] = {"role": role}
+
+        content = m.get("content")
+        images = m.get("images") if isinstance(m.get("images"), list) else []
+        parts: List[Dict[str, Any]] = []
+        if isinstance(content, list):
+            for p in content:
+                if isinstance(p, dict) and p.get("type") == "text" and isinstance(p.get("text"), str):
+                    parts.append({"type": "text", "text": p.get("text")})
+        elif isinstance(content, str):
+            parts.append({"type": "text", "text": content})
+        for img in images:
+            url = to_data_url(img)
+            if isinstance(url, str) and url:
+                parts.append({"type": "image_url", "image_url": {"url": url}})
+        if parts:
+            nm["content"] = parts
+
+        if role == "assistant" and isinstance(m.get("tool_calls"), list):
+            tcs = []
+            for tc in m.get("tool_calls"):
+                if not isinstance(tc, dict):
+                    continue
+                fn = tc.get("function") if isinstance(tc.get("function"), dict) else {}
+                name = fn.get("name") if isinstance(fn.get("name"), str) else None
+                args = fn.get("arguments")
+                if name is None:
+                    continue
+                call_id = tc.get("id") or tc.get("call_id")
+                if not isinstance(call_id, str) or not call_id:
+                    call_counter += 1
+                    call_id = f"ollama_call_{call_counter}"
+                pending_call_ids.append(call_id)
+                tcs.append(
+                    {
+                        "id": call_id,
+                        "type": "function",
+                        "function": {
+                            "name": name,
+                            "arguments": args if isinstance(args, str) else (json.dumps(args) if isinstance(args, dict) else "{}"),
+                        },
+                    }
+                )
+            if tcs:
+                nm["tool_calls"] = tcs
+
+        if role == "tool":
+            tci = m.get("tool_call_id") or m.get("id")
+            if not isinstance(tci, str) or not tci:
+                if pending_call_ids:
+                    tci = pending_call_ids.pop(0)
+            if isinstance(tci, str) and tci:
+                nm["tool_call_id"] = tci
+
+            if not parts and isinstance(content, str):
+                nm["content"] = content
+
+        out.append(nm)
+
+    if isinstance(top_images, list) and top_images:
+        attach_to = None
+        for i in range(len(out) - 1, -1, -1):
+            if out[i].get("role") == "user":
+                attach_to = out[i]
+                break
+        if attach_to is None:
+            attach_to = {"role": "user", "content": []}
+            out.append(attach_to)
+        attach_to.setdefault("content", [])
+        for img in top_images:
+            url = to_data_url(img)
+            if isinstance(url, str) and url:
+                attach_to["content"].append({"type": "image_url", "image_url": {"url": url}})
+    return out
+
+
+def normalize_ollama_tools(tools: List[Dict[str, Any]] | None) -> List[Dict[str, Any]]:
+    out: List[Dict[str, Any]] = []
+    if not isinstance(tools, list):
+        return out
+    for t in tools:
+        if not isinstance(t, dict):
+            continue
+        if isinstance(t.get("function"), dict):
+            fn = t.get("function")
+            name = fn.get("name") if isinstance(fn.get("name"), str) else None
+            if not name:
+                continue
+            out.append(
+                {
+                    "type": "function",
+                    "function": {
+                        "name": name,
+                        "description": fn.get("description") or "",
+                        "parameters": fn.get("parameters") if isinstance(fn.get("parameters"), dict) else {"type": "object", "properties": {}},
+                    },
+                }
+            )
+            continue
+        name = t.get("name") if isinstance(t.get("name"), str) else None
+        if name:
+            out.append(
+                {
+                    "type": "function",
+                    "function": {
+                        "name": name,
+                        "description": t.get("description") or "",
+                        "parameters": {"type": "object", "properties": {}},
+                    },
+                }
+            )
+    return out
+

chatmock/upstream.py

@@ -0,0 +1,99 @@
+from __future__ import annotations
+
+import json
+import time
+from typing import Any, Dict, List, Tuple
+
+import requests
+from flask import Response, jsonify, make_response
+
+from .config import CHATGPT_RESPONSES_URL
+from .http import build_cors_headers
+from .utils import get_effective_chatgpt_auth
+
+
+def normalize_model_name(name: str | None, debug_model: str | None = None) -> str:
+    if isinstance(debug_model, str) and debug_model.strip():
+        return debug_model.strip()
+    if not isinstance(name, str) or not name.strip():
+        return "gpt-5"
+    base = name.split(":", 1)[0].strip()
+    mapping = {
+        "gpt5": "gpt-5",
+        "gpt-5-latest": "gpt-5",
+        "gpt-5": "gpt-5",
+        "codex": "codex-mini-latest",
+        "codex-mini": "codex-mini-latest",
+        "codex-mini-latest": "codex-mini-latest",
+    }
+    return mapping.get(base, base)
+
+
+def start_upstream_request(
+    model: str,
+    input_items: List[Dict[str, Any]],
+    *,
+    instructions: str | None = None,
+    tools: List[Dict[str, Any]] | None = None,
+    tool_choice: Any | None = None,
+    parallel_tool_calls: bool = False,
+    reasoning_param: Dict[str, Any] | None = None,
+):
+    access_token, account_id = get_effective_chatgpt_auth()
+    if not access_token or not account_id:
+        resp = make_response(
+            jsonify(
+                {
+                    "error": {
+                        "message": "Missing ChatGPT credentials. Run 'python3 chatmock.py login' first.",
+                    }
+                }
+            ),
+            401,
+        )
+        for k, v in build_cors_headers().items():
+            resp.headers.setdefault(k, v)
+        return None, resp
+
+    include: List[str] = []
+    if isinstance(reasoning_param, dict) and reasoning_param.get("effort") != "none":
+        include.append("reasoning.encrypted_content")
+
+    responses_payload = {
+        "model": model,
+        "instructions": instructions if isinstance(instructions, str) and instructions.strip() else instructions,
+        "input": input_items,
+        "tools": tools or [],
+        "tool_choice": tool_choice if tool_choice in ("auto", "none") or isinstance(tool_choice, dict) else "auto",
+        "parallel_tool_calls": bool(parallel_tool_calls),
+        "store": False,
+        "stream": True,
+        "include": include,
+    }
+
+    if reasoning_param is not None:
+        responses_payload["reasoning"] = reasoning_param
+
+    headers = {
+        "Authorization": f"Bearer {access_token}",
+        "Content-Type": "application/json",
+        "Accept": "text/event-stream",
+        "chatgpt-account-id": account_id,
+        "OpenAI-Beta": "responses=experimental",
+    }
+
+    try:
+        upstream = requests.post(
+            CHATGPT_RESPONSES_URL,
+            headers=headers,
+            json=responses_payload,
+            stream=True,
+            timeout=600,
+        )
+    except requests.RequestException as e:
+        resp = make_response(jsonify({"error": {"message": f"Upstream ChatGPT request failed: {e}"}}), 502)
+        for k, v in build_cors_headers().items():
+            resp.headers.setdefault(k, v)
+        return None, resp
+    return upstream, None
+

chatmock/utils.py

@@ -72,7 +72,7 @@ def parse_jwt_claims(token: str) -> Dict[str, Any] | None:
 
 
 def generate_pkce() -> "PkceCodes":
-    from models import PkceCodes
+    from .models import PkceCodes
 
     code_verifier = secrets.token_hex(64)
     digest = hashlib.sha256(code_verifier.encode()).digest()
@@ -399,8 +399,6 @@ def sse_translate_chat(
                             "choices": [{"index": 0, "delta": {"content": delta_txt}, "finish_reason": None}],
                         }
                         yield f"data: {json.dumps(content_chunk)}\n\n".encode("utf-8")
-                    else:
-                        pass
                 else:
                     if kind == "response.reasoning_summary_text.delta":
                         chunk = {
@@ -515,3 +513,4 @@ def sse_translate_text(upstream, model: str, created: int, verbose: bool = False
                 break
     finally:
         upstream.close()
+

gui.py

@@ -7,8 +7,9 @@ import multiprocessing as mp
 
 from PySide6 import QtCore, QtGui, QtWidgets
 
-from chatmock import create_app, cmd_login
-from utils import load_chatgpt_tokens, parse_jwt_claims
+from chatmock.app import create_app
+from chatmock.cli import cmd_login
+from chatmock.utils import load_chatgpt_tokens, parse_jwt_claims
 
 
 def run_server(host: str, port: int, reasoning_effort: str = "medium", reasoning_summary: str = "auto") -> None:
@@ -46,9 +47,11 @@ class ServerProcess(QtCore.QObject):
         ]
         self._proc.start(sys.executable, args)
         self._proc.started.connect(lambda: self.state_changed.emit(True))
+
         def _on_finished(code: int, status: QtCore.QProcess.ExitStatus) -> None:
             self.state_changed.emit(False)
             self._proc = None
+
         self._proc.finished.connect(_on_finished)
 
     def stop(self) -> None:
@@ -314,7 +317,9 @@ class MainWindow(QtWidgets.QMainWindow):
         url_row = QtWidgets.QHBoxLayout()
         url_row.addWidget(QtWidgets.QLabel("Base URL:"))
         self.baseurl = QtWidgets.QLabel("(server not running)")
-        self.baseurl.setTextInteractionFlags(QtCore.Qt.TextSelectableByMouse | QtCore.Qt.TextSelectableByKeyboard)
+        self.baseurl.setTextInteractionFlags(
+            QtCore.Qt.TextSelectableByMouse | QtCore.Qt.TextSelectableByKeyboard
+        )
         self.baseurl.setSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Preferred)
         url_row.addWidget(self.baseurl, 1)
         self.btn_copy = QtWidgets.QPushButton("Copy")
@@ -364,7 +369,9 @@ class MainWindow(QtWidgets.QMainWindow):
             email = id_claims.get("email") or id_claims.get("preferred_username") or "<unknown>"
             plan_raw = (access_claims.get("https://api.openai.com/auth") or {}).get("chatgpt_plan_type") or "unknown"
             plan_map = {"plus": "Plus", "pro": "Pro", "free": "Free", "team": "Team", "enterprise": "Enterprise"}
-            plan = plan_map.get(str(plan_raw).lower(), str(plan_raw).title() if isinstance(plan_raw, str) else "Unknown")
+            plan = plan_map.get(
+                str(plan_raw).lower(), str(plan_raw).title() if isinstance(plan_raw, str) else "Unknown"
+            )
             self.email_value.setText(email)
             self.plan_value.setText(plan)
             self.accid_value.setText(account_id or "-")
@@ -395,7 +402,9 @@ class MainWindow(QtWidgets.QMainWindow):
         if code == 0:
             QtWidgets.QMessageBox.information(self, "Login", "Login successful. You can now start the server.")
         elif code == 13:
-            QtWidgets.QMessageBox.warning(self, "Login", "Login helper port is in use. Close other instances and try again.")
+            QtWidgets.QMessageBox.warning(
+                self, "Login", "Login helper port is in use. Close other instances and try again."
+            )
         else:
             QtWidgets.QMessageBox.critical(self, "Login", "Login failed. Please try again.")
         self._refresh_login_state()
@@ -425,7 +434,9 @@ class MainWindow(QtWidgets.QMainWindow):
             self.status.setText("Serving • Running in background")
             self.baseurl.setText(self._server.base_url())
             self.hide()
-            self.tray.showMessage("ChatMock", "Server is running in the background", QtWidgets.QSystemTrayIcon.Information, 1500)
+            self.tray.showMessage(
+                "ChatMock", "Server is running in the background", QtWidgets.QSystemTrayIcon.Information, 1500
+            )
         else:
             self.status.setText("Server stopped")
             self.baseurl.setText("(server not running)")
@@ -450,6 +461,7 @@ def main() -> None:
     mp.freeze_support()
     if "--run-server" in sys.argv:
         import argparse
+
         p = argparse.ArgumentParser(add_help=False)
         p.add_argument("--run-server", action="store_true")
         p.add_argument("--host", default="127.0.0.1")
@@ -469,3 +481,4 @@ def main() -> None:
 
 if __name__ == "__main__":
     main()
+