import os

path = 'ai_client.py'
with open(path, 'r', encoding='utf-8') as f:
 lines = f.readlines()

# Very basic cleanup: remove lines after the first 'def get_history_bleed_stats' 
# or other markers of duplication if they exist.
# Actually, I'll just rewrite the relevant functions and clean up the end of the file.

new_lines = []
skip = False
for line in lines:
 if 'def _send_gemini(' in line and 'stream_callback' in line:
  # This is my partially applied change, I'll keep it but fix it.
  pass
 if 'def send(' in line and 'import json' in lines[lines.index(line)-1]:
  # This looks like the duplicated send at the end
  skip = True
 if not skip:
  new_lines.append(line)
 if skip and 'return {' in line and 'percentage' in line:
  # End of duplicated get_history_bleed_stats
  # skip = False # actually just keep skipping till the end
  pass

# It's better to just surgically fix the file content in memory.
content = "".join(new_lines)

# I'll use a more robust approach: I'll define the final versions of the functions I want to change.

_SEND_GEMINI_NEW = '''def _send_gemini(md_content: str, user_message: str, base_dir: str,
 file_items: list[dict[str, Any]] | None = None,
 discussion_history: str = "",
 pre_tool_callback: Optional[Callable[[str], bool]] = None,
 qa_callback: Optional[Callable[[str], str]] = None,
 enable_tools: bool = True,
 stream_callback: Optional[Callable[[str], None]] = None) -> str:
 global _gemini_chat, _gemini_cache, _gemini_cache_md_hash, _gemini_cache_created_at
 try:
  _ensure_gemini_client(); mcp_client.configure(file_items or [], [base_dir])
  # Only stable content (files + screenshots) goes in the cached system instruction.
  # Discussion history is sent as conversation messages so the cache isn't invalidated every turn.
  sys_instr = f"{_get_combined_system_prompt()}

<context>
{md_content}
</context>"
  td = _gemini_tool_declaration() if enable_tools else None
  tools_decl = [td] if td else None
  # DYNAMIC CONTEXT: Check if files/context changed mid-session
  current_md_hash = hashlib.md5(md_content.encode()).hexdigest()
  old_history = None
  if _gemini_chat and _gemini_cache_md_hash != current_md_hash:
   old_history = list(_get_gemini_history_list(_gemini_chat)) if _get_gemini_history_list(_gemini_chat) else []
   if _gemini_cache:
    try: _gemini_client.caches.delete(name=_gemini_cache.name)
    except Exception as e: _append_comms("OUT", "request", {"message": f"[CACHE DELETE WARN] {e}"})
   _gemini_chat = None
   _gemini_cache = None
   _gemini_cache_created_at = None
   _append_comms("OUT", "request", {"message": "[CONTEXT CHANGED] Rebuilding cache and chat session..."})
  if _gemini_chat and _gemini_cache and _gemini_cache_created_at:
   elapsed = time.time() - _gemini_cache_created_at
   if elapsed > _GEMINI_CACHE_TTL * 0.9:
    old_history = list(_get_gemini_history_list(_gemini_chat)) if _get_gemini_history_list(_get_gemini_history_list(_gemini_chat)) else []
    try: _gemini_client.caches.delete(name=_gemini_cache.name)
    except Exception as e: _append_comms("OUT", "request", {"message": f"[CACHE DELETE WARN] {e}"})
    _gemini_chat = None
    _gemini_cache = None
    _gemini_cache_created_at = None
    _append_comms("OUT", "request", {"message": f"[CACHE TTL] Rebuilding cache (expired after {int(elapsed)}s)..."})
  if not _gemini_chat:
   chat_config = types.GenerateContentConfig(
    system_instruction=sys_instr,
    tools=tools_decl,
    temperature=_temperature,
    max_output_tokens=_max_tokens,
    safety_settings=[types.SafetySetting(category="HARM_CATEGORY_DANGEROUS_CONTENT", threshold="BLOCK_ONLY_HIGH")]
   )
   should_cache = False
   try:
    count_resp = _gemini_client.models.count_tokens(model=_model, contents=[sys_instr])
    if count_resp.total_tokens >= 2048:
     should_cache = True
    else:
     _append_comms("OUT", "request", {"message": f"[CACHING SKIPPED] Context too small ({count_resp.total_tokens} tokens < 2048)"})
   except Exception as e:
    _append_comms("OUT", "request", {"message": f"[COUNT FAILED] {e}"})
   if should_cache:
    try:
     _gemini_cache = _gemini_client.caches.create(
      model=_model,
      config=types.CreateCachedContentConfig(
       system_instruction=sys_instr,
       tools=tools_decl,
       ttl=f"{_GEMINI_CACHE_TTL}s",
      )
     )
     _gemini_cache_created_at = time.time()
     chat_config = types.GenerateContentConfig(
      cached_content=_gemini_cache.name,
      temperature=_temperature,
      max_output_tokens=_max_tokens,
      safety_settings=[types.SafetySetting(category="HARM_CATEGORY_DANGEROUS_CONTENT", threshold="BLOCK_ONLY_HIGH")]
     )
     _append_comms("OUT", "request", {"message": f"[CACHE CREATED] {_gemini_cache.name}"})
    except Exception as e:
     _gemini_cache = None
     _gemini_cache_created_at = None
     _append_comms("OUT", "request", {"message": f"[CACHE FAILED] {type(e).__name__}: {e} \u2014 falling back to inline system_instruction"})
   kwargs = {"model": _model, "config": chat_config}
   if old_history:
    kwargs["history"] = old_history
   _gemini_chat = _gemini_client.chats.create(**kwargs)
   _gemini_cache_md_hash = current_md_hash
   if discussion_history and not old_history:
    _gemini_chat.send_message(f"[DISCUSSION HISTORY]

{discussion_history}")
    _append_comms("OUT", "request", {"message": f"[HISTORY INJECTED] {len(discussion_history)} chars"})
  _append_comms("OUT", "request", {"message": f"[ctx {len(md_content)} + msg {len(user_message)}]"})
  payload: str | list[types.Part] = user_message
  all_text: list[str] = []
  _cumulative_tool_bytes = 0
  if _gemini_chat and _get_gemini_history_list(_gemini_chat):
   for msg in _get_gemini_history_list(_gemini_chat):
    if msg.role == "user" and hasattr(msg, "parts"):
     for p in msg.parts:
      if hasattr(p, "function_response") and p.function_response and hasattr(p.function_response, "response"):
       r = p.function_response.response
       if isinstance(r, dict) and "output" in r:
        val = r["output"]
        if isinstance(val, str):
         if "[SYSTEM: FILES UPDATED]" in val:
          val = val.split("[SYSTEM: FILES UPDATED]")[0].strip()
         if _history_trunc_limit > 0 and len(val) > _history_trunc_limit:
          val = val[:_history_trunc_limit] + "

... [TRUNCATED BY SYSTEM TO SAVE TOKENS.]"
         r["output"] = val
  for r_idx in range(MAX_TOOL_ROUNDS + 2):
   events.emit("request_start", payload={"provider": "gemini", "model": _model, "round": r_idx})
   if stream_callback:
    resp = _gemini_chat.send_message_stream(payload)
    txt_chunks = []
    for chunk in resp:
     c_txt = chunk.text
     if c_txt:
      txt_chunks.append(c_txt)
      stream_callback(c_txt)
    txt = "".join(txt_chunks)
    calls = [p.function_call for c in resp.candidates if getattr(c, "content", None) for p in c.content.parts if hasattr(p, "function_call") and p.function_call]
    usage = {"input_tokens": getattr(resp.usage_metadata, "prompt_token_count", 0), "output_tokens": getattr(resp.usage_metadata, "candidates_token_count", 0)}
    cached_tokens = getattr(resp.usage_metadata, "cached_content_token_count", None)
    if cached_tokens: usage["cache_read_input_tokens"] = cached_tokens
   else:
    resp = _gemini_chat.send_message(payload)
    txt = "
".join(p.text for c in resp.candidates if getattr(c, "content", None) for p in c.content.parts if hasattr(p, "text") and p.text)
    calls = [p.function_call for c in resp.candidates if getattr(c, "content", None) for p in c.content.parts if hasattr(p, "function_call") and p.function_call]
    usage = {"input_tokens": getattr(resp.usage_metadata, "prompt_token_count", 0), "output_tokens": getattr(resp.usage_metadata, "candidates_token_count", 0)}
    cached_tokens = getattr(resp.usage_metadata, "cached_content_token_count", None)
    if cached_tokens: usage["cache_read_input_tokens"] = cached_tokens
   if txt: all_text.append(txt)
   events.emit("response_received", payload={"provider": "gemini", "model": _model, "usage": usage, "round": r_idx})
   reason = resp.candidates[0].finish_reason.name if resp.candidates and hasattr(resp.candidates[0], "finish_reason") else "STOP"
   _append_comms("IN", "response", {"round": r_idx, "stop_reason": reason, "text": txt, "tool_calls": [{"name": c.name, "args": dict(c.args)} for c in calls], "usage": usage})
   total_in = usage.get("input_tokens", 0)
   if total_in > _GEMINI_MAX_INPUT_TOKENS * 0.4 and _gemini_chat and _get_gemini_history_list(_gemini_chat):
    hist = _get_gemini_history_list(_gemini_chat)
    dropped = 0
    while len(hist) > 4 and total_in > _GEMINI_MAX_INPUT_TOKENS * 0.3:
     saved = 0
     for _ in range(2):
      if not hist: break
      for p in hist[0].parts:
       if hasattr(p, "text") and p.text: saved += int(len(p.text) / _CHARS_PER_TOKEN)
       elif hasattr(p, "function_response") and p.function_response:
        r = getattr(p.function_response, "response", {})
        if isinstance(r, dict): saved += int(len(str(r.get("output", ""))) / _CHARS_PER_TOKEN)
      hist.pop(0)
      dropped += 1
     total_in -= max(saved, 200)
    if dropped > 0: _append_comms("OUT", "request", {"message": f"[GEMINI HISTORY TRIMMED: dropped {dropped} old entries]"})
   if not calls or r_idx > MAX_TOOL_ROUNDS: break
   f_resps: list[types.Part] = []
   log: list[dict[str, Any]] = []
   for i, fc in enumerate(calls):
    name, args = fc.name, dict(fc.args)
    if pre_tool_callback:
     payload_str = json.dumps({"tool": name, "args": args})
     if not pre_tool_callback(payload_str):
      out = "USER REJECTED: tool execution cancelled"
      f_resps.append(types.Part.from_function_response(name=name, response={"output": out}))
      log.append({"tool_use_id": name, "content": out})
      continue
    events.emit("tool_execution", payload={"status": "started", "tool": name, "args": args, "round": r_idx})
    if name in mcp_client.TOOL_NAMES:
     _append_comms("OUT", "tool_call", {"name": name, "args": args})
     out = mcp_client.dispatch(name, args)
    elif name == TOOL_NAME:
     scr = args.get("script", "")
     _append_comms("OUT", "tool_call", {"name": TOOL_NAME, "script": scr})
     out = _run_script(scr, base_dir, qa_callback)
    else: out = f"ERROR: unknown tool '{name}'"
    if i == len(calls) - 1:
     if file_items:
      file_items, changed = _reread_file_items(file_items)
      ctx = _build_file_diff_text(changed)
      if ctx: out += f"

[SYSTEM: FILES UPDATED]

{ctx}"
     if r_idx == MAX_TOOL_ROUNDS: out += "

[SYSTEM: MAX ROUNDS. PROVIDE FINAL ANSWER.]"
    out = _truncate_tool_output(out)
    _cumulative_tool_bytes += len(out)
    f_resps.append(types.Part.from_function_response(name=name, response={"output": out}))
    log.append({"tool_use_id": name, "content": out})
    events.emit("tool_execution", payload={"status": "completed", "tool": name, "result": out, "round": r_idx})
   if _cumulative_tool_bytes > _MAX_TOOL_OUTPUT_BYTES:
    f_resps.append(types.Part.from_text(f"SYSTEM WARNING: Cumulative tool output exceeded {_MAX_TOOL_OUTPUT_BYTES // 1000}KB budget."))
    _append_comms("OUT", "request", {"message": f"[TOOL OUTPUT BUDGET EXCEEDED: {_cumulative_tool_bytes} bytes]"})
   _append_comms("OUT", "tool_result_send", {"results": log})
   payload = f_resps
  return "

".join(all_text) if all_text else "(No text returned)"
 except Exception as e: raise _classify_gemini_error(e) from e
'''

_SEND_ANTHROPIC_NEW = '''def _send_anthropic(md_content: str, user_message: str, base_dir: str, file_items: list[dict[str, Any]] | None = None, discussion_history: str = "", pre_tool_callback: Optional[Callable[[str], bool]] = None, qa_callback: Optional[Callable[[str], str]] = None, stream_callback: Optional[Callable[[str], None]] = None) -> str:
 try:
  _ensure_anthropic_client()
  mcp_client.configure(file_items or [], [base_dir])
  stable_prompt = _get_combined_system_prompt()
  stable_blocks = [{"type": "text", "text": stable_prompt, "cache_control": {"type": "ephemeral"}}]
  context_text = f"

<context>
{md_content}
</context>"
  context_blocks = _build_chunked_context_blocks(context_text)
  system_blocks = stable_blocks + context_blocks
  if discussion_history and not _anthropic_history:
   user_content: list[dict[str, Any]] = [{"type": "text", "text": f"[DISCUSSION HISTORY]

{discussion_history}

---

{user_message}"}]
  else:
   user_content = [{"type": "text", "text": user_message}]
  for msg in _anthropic_history:
   if msg.get("role") == "user" and isinstance(msg.get("content"), list):
    modified = False
    for block in msg["content"]:
     if isinstance(block, dict) and block.get("type") == "tool_result":
      t_content = block.get("content", "")
      if _history_trunc_limit > 0 and isinstance(t_content, str) and len(t_content) > _history_trunc_limit:
       block["content"] = t_content[:_history_trunc_limit] + "

... [TRUNCATED BY SYSTEM]"
       modified = True
    if modified: _invalidate_token_estimate(msg)
  _strip_cache_controls(_anthropic_history)
  _repair_anthropic_history(_anthropic_history)
  _anthropic_history.append({"role": "user", "content": user_content})
  _add_history_cache_breakpoint(_anthropic_history)
  all_text_parts: list[str] = []
  _cumulative_tool_bytes = 0
  def _strip_private_keys(history: list[dict[str, Any]]) -> list[dict[str, Any]]:
   return [{k: v for k, v in m.items() if not k.startswith("_")} for m in history]
  for round_idx in range(MAX_TOOL_ROUNDS + 2):
   dropped = _trim_anthropic_history(system_blocks, _anthropic_history)
   if dropped > 0:
    est_tokens = _estimate_prompt_tokens(system_blocks, _anthropic_history)
    _append_comms("OUT", "request", {"message": f"[HISTORY TRIMMED: dropped {dropped} old messages]"})
   events.emit("request_start", payload={"provider": "anthropic", "model": _model, "round": round_idx})
   if stream_callback:
    with _anthropic_client.messages.stream(
     model=_model,
     max_tokens=_max_tokens,
     temperature=_temperature,
     system=system_blocks,
     tools=_get_anthropic_tools(),
     messages=_strip_private_keys(_anthropic_history),
    ) as stream:
     for event in stream:
      if event.type == "content_block_delta" and event.delta.type == "text_delta":
       stream_callback(event.delta.text)
     response = stream.get_final_message()
   else:
    response = _anthropic_client.messages.create(
     model=_model,
     max_tokens=_max_tokens,
     temperature=_temperature,
     system=system_blocks,
     tools=_get_anthropic_tools(),
     messages=_strip_private_keys(_anthropic_history),
    )
   serialised_content = [_content_block_to_dict(b) for b in response.content]
   _anthropic_history.append({"role": "assistant", "content": serialised_content})
   text_blocks = [b.text for b in response.content if hasattr(b, "text") and b.text]
   if text_blocks: all_text_parts.append("
".join(text_blocks))
   tool_use_blocks = [{"id": b.id, "name": b.name, "input": b.input} for b in response.content if getattr(b, "type", None) == "tool_use"]
   usage_dict: dict[str, Any] = {}
   if response.usage:
    usage_dict["input_tokens"] = response.usage.input_tokens
    usage_dict["output_tokens"] = response.usage.output_tokens
    for k in ["cache_creation_input_tokens", "cache_read_input_tokens"]:
     val = getattr(response.usage, k, None)
     if val is not None: usage_dict[k] = val
   events.emit("response_received", payload={"provider": "anthropic", "model": _model, "usage": usage_dict, "round": round_idx})
   _append_comms("IN", "response", {"round": round_idx, "stop_reason": response.stop_reason, "text": "
".join(text_blocks), "tool_calls": tool_use_blocks, "usage": usage_dict})
   if response.stop_reason != "tool_use" or not tool_use_blocks: break
   if round_idx > MAX_TOOL_ROUNDS: break
   tool_results: list[dict[str, Any]] = []
   for block in response.content:
    if getattr(block, "type", None) != "tool_use": continue
    b_name, b_id, b_input = block.name, block.id, block.input
    if pre_tool_callback:
     if not pre_tool_callback(json.dumps({"tool": b_name, "args": b_input})):
      tool_results.append({"type": "tool_result", "tool_use_id": b_id, "content": "USER REJECTED: tool execution cancelled"})
      continue
    events.emit("tool_execution", payload={"status": "started", "tool": b_name, "args": b_input, "round": round_idx})
    if b_name in mcp_client.TOOL_NAMES:
     _append_comms("OUT", "tool_call", {"name": b_name, "id": b_id, "args": b_input})
     output = mcp_client.dispatch(b_name, b_input)
    elif b_name == TOOL_NAME:
     scr = b_input.get("script", "")
     _append_comms("OUT", "tool_call", {"name": TOOL_NAME, "id": b_id, "script": scr})
     output = _run_script(scr, base_dir, qa_callback)
    else: output = f"ERROR: unknown tool '{b_name}'"
    truncated = _truncate_tool_output(output)
    _cumulative_tool_bytes += len(truncated)
    tool_results.append({"type": "tool_result", "tool_use_id": b_id, "content": truncated})
    _append_comms("IN", "tool_result", {"name": b_name, "id": b_id, "output": output})
    events.emit("tool_execution", payload={"status": "completed", "tool": b_name, "result": output, "round": round_idx})
   if _cumulative_tool_bytes > _MAX_TOOL_OUTPUT_BYTES:
    tool_results.append({"type": "text", "text": "SYSTEM WARNING: Cumulative tool output exceeded budget."})
   if file_items:
    file_items, changed = _reread_file_items(file_items)
    refreshed_ctx = _build_file_diff_text(changed)
    if refreshed_ctx: tool_results.append({"type": "text", "text": f"[FILES UPDATED]

{refreshed_ctx}"})
   if round_idx == MAX_TOOL_ROUNDS: tool_results.append({"type": "text", "text": "SYSTEM WARNING: MAX TOOL ROUNDS REACHED."})
   _anthropic_history.append({"role": "user", "content": tool_results})
   _append_comms("OUT", "tool_result_send", {"results": [{"tool_use_id": r["tool_use_id"], "content": r["content"]} for r in tool_results if r.get("type") == "tool_result"]})
  return "

".join(all_text_parts) if all_text_parts else "(No text returned)"
 except Exception as exc: raise _classify_anthropic_error(exc) from exc
'''

_SEND_DEEPSEEK_NEW = '''def _send_deepseek(md_content: str, user_message: str, base_dir: str,
 file_items: list[dict[str, Any]] | None = None,
 discussion_history: str = "",
 stream: bool = False,
 pre_tool_callback: Optional[Callable[[str], bool]] = None,
 qa_callback: Optional[Callable[[str], str]] = None,
 stream_callback: Optional[Callable[[str], None]] = None) -> str:
 try:
  mcp_client.configure(file_items or [], [base_dir])
  creds = _load_credentials()
  api_key = creds.get("deepseek", {}).get("api_key")
  if not api_key: raise ValueError("DeepSeek API key not found")
  api_url = "https://api.deepseek.com/chat/completions"
  headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
  current_api_messages: list[dict[str, Any]] = []
  with _deepseek_history_lock:
   for msg in _deepseek_history: current_api_messages.append(msg)
  initial_user_message_content = user_message
  if discussion_history: initial_user_message_content = f"[DISCUSSION HISTORY]

{discussion_history}

---

{user_message}"
  current_api_messages.append({"role": "user", "content": initial_user_message_content})
  request_payload: dict[str, Any] = {"model": _model, "messages": current_api_messages, "temperature": _temperature, "max_tokens": _max_tokens, "stream": stream}
  sys_msg = {"role": "system", "content": f"{_get_combined_system_prompt()}

<context>
{md_content}
</context>"}
  request_payload["messages"].insert(0, sys_msg)
  all_text_parts: list[str] = []
  _cumulative_tool_bytes = 0
  round_idx = 0
  while round_idx <= MAX_TOOL_ROUNDS + 1:
   events.emit("request_start", payload={"provider": "deepseek", "model": _model, "round": round_idx, "streaming": stream})
   try:
    response = requests.post(api_url, headers=headers, json=request_payload, timeout=60, stream=stream)
    response.raise_for_status()
   except requests.exceptions.RequestException as e: raise _classify_deepseek_error(e) from e
   if stream:
    aggregated_content, aggregated_tool_calls, aggregated_reasoning = "", [], ""
    current_usage, final_finish_reason = {}, "stop"
    for line in response.iter_lines():
     if not line: continue
     decoded = line.decode('utf-8')
     if decoded.startswith('data: '):
      chunk_str = decoded[len('data: '):]
      if chunk_str.strip() == '[DONE]': continue
      try:
       chunk = json.loads(chunk_str)
       delta = chunk.get("choices", [{}])[0].get("delta", {})
       if delta.get("content"):
        aggregated_content += delta["content"]
        if stream_callback: stream_callback(delta["content"])
       if delta.get("reasoning_content"): aggregated_reasoning += delta["reasoning_content"]
       if delta.get("tool_calls"):
        for tc_delta in delta["tool_calls"]:
         idx = tc_delta.get("index", 0)
         while len(aggregated_tool_calls) <= idx: aggregated_tool_calls.append({"id": "", "type": "function", "function": {"name": "", "arguments": ""}})
         target = aggregated_tool_calls[idx]
         if tc_delta.get("id"): target["id"] = tc_delta["id"]
         if tc_delta.get("function", {}).get("name"): target["function"]["name"] += tc_delta["function"]["name"]
         if tc_delta.get("function", {}).get("arguments"): target["function"]["arguments"] += tc_delta["function"]["arguments"]
       if chunk.get("choices", [{}])[0].get("finish_reason"): final_finish_reason = chunk["choices"][0]["finish_reason"]
       if chunk.get("usage"): current_usage = chunk["usage"]
      except json.JSONDecodeError: continue
    assistant_text, tool_calls_raw, reasoning_content, finish_reason, usage = aggregated_content, aggregated_tool_calls, aggregated_reasoning, final_finish_reason, current_usage
   else:
    response_data = response.json()
    choices = response_data.get("choices", [])
    if not choices: break
    choice = choices[0]
    message = choice.get("message", {})
    assistant_text, tool_calls_raw, reasoning_content, finish_reason, usage = message.get("content", ""), message.get("tool_calls", []), message.get("reasoning_content", ""), choice.get("finish_reason", "stop"), response_data.get("usage", {})
   full_assistant_text = (f"<thinking>
{reasoning_content}
</thinking>
" if reasoning_content else "") + assistant_text
   with _deepseek_history_lock:
    msg_to_store = {"role": "assistant", "content": assistant_text}
    if reasoning_content: msg_to_store["reasoning_content"] = reasoning_content
    if tool_calls_raw: msg_to_store["tool_calls"] = tool_calls_raw
    _deepseek_history.append(msg_to_store)
   if full_assistant_text: all_text_parts.append(full_assistant_text)
   _append_comms("IN", "response", {"round": round_idx, "stop_reason": finish_reason, "text": full_assistant_text, "tool_calls": tool_calls_raw, "usage": usage, "streaming": stream})
   if finish_reason != "tool_calls" and not tool_calls_raw: break
   if round_idx > MAX_TOOL_ROUNDS: break
   tool_results_for_history: list[dict[str, Any]] = []
   for i, tc_raw in enumerate(tool_calls_raw):
    tool_info = tc_raw.get("function", {})
    tool_name, tool_args_str, tool_id = tool_info.get("name"), tool_info.get("arguments", "{}"), tc_raw.get("id")
    try: tool_args = json.loads(tool_args_str)
    except: tool_args = {}
    if pre_tool_callback:
     if not pre_tool_callback(json.dumps({"tool": tool_name, "args": tool_args})):
      tool_output = "USER REJECTED: tool execution cancelled"
      tool_results_for_history.append({"role": "tool", "tool_call_id": tool_id, "content": tool_output})
      continue
    events.emit("tool_execution", payload={"status": "started", "tool": tool_name, "args": tool_args, "round": round_idx})
    if tool_name in mcp_client.TOOL_NAMES:
     _append_comms("OUT", "tool_call", {"name": tool_name, "id": tool_id, "args": tool_args})
     tool_output = mcp_client.dispatch(tool_name, tool_args)
    elif tool_name == TOOL_NAME:
     script = tool_args.get("script", "")
     _append_comms("OUT", "tool_call", {"name": TOOL_NAME, "id": tool_id, "script": script})
     tool_output = _run_script(script, base_dir, qa_callback)
    else: tool_output = f"ERROR: unknown tool '{tool_name}'"
    if i == len(tool_calls_raw) - 1:
     if file_items:
      file_items, changed = _reread_file_items(file_items)
      ctx = _build_file_diff_text(changed)
      if ctx: tool_output += f"

[SYSTEM: FILES UPDATED]

{ctx}"
     if round_idx == MAX_TOOL_ROUNDS: tool_output += "

[SYSTEM: MAX ROUNDS. PROVIDE FINAL ANSWER.]"
    tool_output = _truncate_tool_output(tool_output)
    _cumulative_tool_bytes += len(tool_output)
    tool_results_for_history.append({"role": "tool", "tool_call_id": tool_id, "content": tool_output})
    _append_comms("IN", "tool_result", {"name": tool_name, "id": tool_id, "output": tool_output})
    events.emit("tool_execution", payload={"status": "completed", "tool": tool_name, "result": tool_output, "round": round_idx})
   if _cumulative_tool_bytes > _MAX_TOOL_OUTPUT_BYTES:
    tool_results_for_history.append({"role": "user", "content": "SYSTEM WARNING: Cumulative tool output exceeded budget."})
   with _deepseek_history_lock:
    for tr in tool_results_for_history: _deepseek_history.append(tr)
   next_messages: list[dict[str, Any]] = []
   with _deepseek_history_lock:
    for msg in _deepseek_history: next_messages.append(msg)
   next_messages.insert(0, sys_msg)
   request_payload["messages"] = next_messages
   round_idx += 1
  return "

".join(all_text_parts) if all_text_parts else "(No text returned)"
 except Exception as e: raise _classify_deepseek_error(e) from e
'''

_SEND_NEW = '''def send(
 md_content: str,
 user_message: str,
 base_dir: str = ".",
 file_items: list[dict[str, Any]] | None = None,
 discussion_history: str = "",
 stream: bool = False,
 pre_tool_callback: Optional[Callable[[str], bool]] = None,
 qa_callback: Optional[Callable[[str], str]] = None,
 enable_tools: bool = True,
 stream_callback: Optional[Callable[[str], None]] = None,
) -> str:
 """
    Sends a prompt with the full markdown context to the current AI provider.
    Returns the final text response.
    """
 with _send_lock:
  if _provider == "gemini":
   return _send_gemini(
    md_content, user_message, base_dir, file_items, discussion_history,
    pre_tool_callback, qa_callback, enable_tools, stream_callback
   )
  elif _provider == "gemini_cli":
   return _send_gemini_cli(
    md_content, user_message, base_dir, file_items, discussion_history,
    pre_tool_callback, qa_callback
   )
  elif _provider == "anthropic":
   return _send_anthropic(
    md_content, user_message, base_dir, file_items, discussion_history,
    pre_tool_callback, qa_callback, stream_callback=stream_callback
   )
  elif _provider == "deepseek":
   return _send_deepseek(
    md_content, user_message, base_dir, file_items, discussion_history,
    stream, pre_tool_callback, qa_callback, stream_callback
   )
  else:
   raise ValueError(f"Unknown provider: {_provider}")
'''

# Use regex or simple string replacement to replace the old functions with new ones.
import re

def replace_func(content, func_name, new_body):
 # This is tricky because functions can be complex.
 # I'll just use a marker based approach for this specific file.
 start_marker = f'def {func_name}('
 # Find the next 'def ' or end of file
 start_idx = content.find(start_marker)
 if start_idx == -1: return content
 
 # Find the end of the function (rough estimation based on next def at column 0)
 next_def = re.search(r'

def ', content[start_idx+1:])
 if next_def:
  end_idx = start_idx + 1 + next_def.start()
 else:
  end_idx = len(content)
 
 return content[:start_idx] + new_body + content[end_idx:]

# Final content construction
content = replace_func(content, '_send_gemini', _SEND_GEMINI_NEW)
content = replace_func(content, '_send_anthropic', _SEND_ANTHROPIC_NEW)
content = replace_func(content, '_send_deepseek', _SEND_DEEPSEEK_NEW)
content = replace_func(content, 'send', _SEND_NEW)

# Remove the duplicated parts at the end if any
marker = 'import json
from typing import Any, Callable, Optional, List'
if marker in content:
 content = content[:content.find(marker)]

with open(path, 'w', encoding='utf-8') as f:
 f.write(content)