app/api_helpers.py

import json
import time
import math
import asyncio
import base64 
from typing import List, Dict, Any, Callable, Union, Optional

from fastapi.responses import JSONResponse, StreamingResponse
from google.auth.transport.requests import Request as AuthRequest
from google.genai import types
from google.genai.types import HttpOptions 
from google import genai # Original import
from openai import AsyncOpenAI

from models import OpenAIRequest, OpenAIMessage
from message_processing import (
    deobfuscate_text, 
    convert_to_openai_format, 
    convert_chunk_to_openai, 
    create_final_chunk,
    split_text_by_completion_tokens,
    parse_gemini_response_for_reasoning_and_content, # Added import
    extract_reasoning_by_tags # Added for new OpenAI direct reasoning logic
)
import config as app_config

def create_openai_error_response(status_code: int, message: str, error_type: str) -> Dict[str, Any]:
    return {
        "error": {
            "message": message,
            "type": error_type,
            "code": status_code,
            "param": None,
        }
    }

def create_generation_config(request: OpenAIRequest) -> Dict[str, Any]:
    config = {}
    if request.temperature is not None: config["temperature"] = request.temperature
    if request.max_tokens is not None: config["max_output_tokens"] = request.max_tokens
    if request.top_p is not None: config["top_p"] = request.top_p
    if request.top_k is not None: config["top_k"] = request.top_k
    if request.stop is not None: config["stop_sequences"] = request.stop
    if request.seed is not None: config["seed"] = request.seed
    if request.presence_penalty is not None: config["presence_penalty"] = request.presence_penalty
    if request.frequency_penalty is not None: config["frequency_penalty"] = request.frequency_penalty
    if request.n is not None: config["candidate_count"] = request.n
    config["safety_settings"] = [
            types.SafetySetting(category="HARM_CATEGORY_HATE_SPEECH", threshold="OFF"),
            types.SafetySetting(category="HARM_CATEGORY_DANGEROUS_CONTENT", threshold="OFF"),
            types.SafetySetting(category="HARM_CATEGORY_SEXUALLY_EXPLICIT", threshold="OFF"),
            types.SafetySetting(category="HARM_CATEGORY_HARASSMENT", threshold="OFF"),
            types.SafetySetting(category="HARM_CATEGORY_CIVIC_INTEGRITY", threshold="OFF")
    ]
    return config

def is_gemini_response_valid(response: Any) -> bool:
    if response is None: return False
    if hasattr(response, 'text') and isinstance(response.text, str) and response.text.strip(): return True
    if hasattr(response, 'candidates') and response.candidates:
        for candidate in response.candidates:
            if hasattr(candidate, 'text') and isinstance(candidate.text, str) and candidate.text.strip(): return True
            if hasattr(candidate, 'content') and hasattr(candidate.content, 'parts') and candidate.content.parts:
                for part_item in candidate.content.parts: 
                    if hasattr(part_item, 'text') and isinstance(part_item.text, str) and part_item.text.strip(): return True
    return False

async def _base_fake_stream_engine(
    api_call_task_creator: Callable[[], asyncio.Task],
    extract_text_from_response_func: Callable[[Any], str], 
    response_id: str,
    sse_model_name: str,
    is_auto_attempt: bool,
    is_valid_response_func: Callable[[Any], bool],
    keep_alive_interval_seconds: float, 
    process_text_func: Optional[Callable[[str, str], str]] = None, 
    check_block_reason_func: Optional[Callable[[Any], None]] = None,
    reasoning_text_to_yield: Optional[str] = None,
    actual_content_text_to_yield: Optional[str] = None
):
    api_call_task = api_call_task_creator()

    if keep_alive_interval_seconds > 0:
        while not api_call_task.done():
            keep_alive_data = {"id": "chatcmpl-keepalive", "object": "chat.completion.chunk", "created": int(time.time()), "model": sse_model_name, "choices": [{"delta": {"reasoning_content": ""}, "index": 0, "finish_reason": None}]}
            yield f"data: {json.dumps(keep_alive_data)}\n\n"
            await asyncio.sleep(keep_alive_interval_seconds) 
    
    try:
        full_api_response = await api_call_task 

        if check_block_reason_func:
            check_block_reason_func(full_api_response) 

        if not is_valid_response_func(full_api_response): 
             raise ValueError(f"Invalid/empty API response in fake stream for model {sse_model_name}: {str(full_api_response)[:200]}")

        final_reasoning_text = reasoning_text_to_yield
        final_actual_content_text = actual_content_text_to_yield

        if final_reasoning_text is None and final_actual_content_text is None:
            extracted_full_text = extract_text_from_response_func(full_api_response)
            if process_text_func:
                final_actual_content_text = process_text_func(extracted_full_text, sse_model_name)
            else:
                final_actual_content_text = extracted_full_text
        else:
            if process_text_func:
                if final_reasoning_text is not None:
                    final_reasoning_text = process_text_func(final_reasoning_text, sse_model_name)
                if final_actual_content_text is not None:
                    final_actual_content_text = process_text_func(final_actual_content_text, sse_model_name)
        
        if final_reasoning_text: 
            reasoning_delta_data = {
                "id": response_id, "object": "chat.completion.chunk", "created": int(time.time()),
                "model": sse_model_name, "choices": [{"index": 0, "delta": {"reasoning_content": final_reasoning_text}, "finish_reason": None}]
            }
            yield f"data: {json.dumps(reasoning_delta_data)}\n\n"
            if final_actual_content_text: 
                await asyncio.sleep(0.05) 

        content_to_chunk = final_actual_content_text or "" 
        chunk_size = max(20, math.ceil(len(content_to_chunk) / 10)) if content_to_chunk else 0
        
        if not content_to_chunk and content_to_chunk != "": 
            empty_delta_data = {"id": response_id, "object": "chat.completion.chunk", "created": int(time.time()), "model": sse_model_name, "choices": [{"index": 0, "delta": {"content": ""}, "finish_reason": None}]}
            yield f"data: {json.dumps(empty_delta_data)}\n\n"
        else: 
            for i in range(0, len(content_to_chunk), chunk_size):
                chunk_text = content_to_chunk[i:i+chunk_size]
                content_delta_data = {"id": response_id, "object": "chat.completion.chunk", "created": int(time.time()), "model": sse_model_name, "choices": [{"index": 0, "delta": {"content": chunk_text}, "finish_reason": None}]}
                yield f"data: {json.dumps(content_delta_data)}\n\n"
                if len(content_to_chunk) > chunk_size: await asyncio.sleep(0.05)

        yield create_final_chunk(sse_model_name, response_id)
        yield "data: [DONE]\n\n"

    except Exception as e:
        err_msg_detail = f"Error in _base_fake_stream_engine (model: '{sse_model_name}'): {type(e).__name__} - {str(e)}"
        print(f"ERROR: {err_msg_detail}")
        sse_err_msg_display = str(e) 
        if len(sse_err_msg_display) > 512: sse_err_msg_display = sse_err_msg_display[:512] + "..."
        err_resp_for_sse = create_openai_error_response(500, sse_err_msg_display, "server_error")
        json_payload_for_fake_stream_error = json.dumps(err_resp_for_sse)
        if not is_auto_attempt:
            yield f"data: {json_payload_for_fake_stream_error}\n\n"
            yield "data: [DONE]\n\n"
        raise

async def gemini_fake_stream_generator( # Changed to async
    gemini_client_instance: Any, 
    model_for_api_call: str, 
    prompt_for_api_call: Union[types.Content, List[types.Content]],
    gen_config_for_api_call: Dict[str, Any],
    request_obj: OpenAIRequest,
    is_auto_attempt: bool
):
    model_name_for_log = getattr(gemini_client_instance, 'model_name', 'unknown_gemini_model_object')
    print(f"FAKE STREAMING (Gemini): Prep for '{request_obj.model}' (API model string: '{model_for_api_call}', client obj: '{model_name_for_log}') with reasoning separation.")
    response_id = f"chatcmpl-{int(time.time())}"

    # 1. Create and await the API call task
    api_call_task = asyncio.create_task(
        gemini_client_instance.aio.models.generate_content(
            model=model_for_api_call, 
            contents=prompt_for_api_call, 
            config=gen_config_for_api_call
        )
    )

    # Keep-alive loop while the main API call is in progress
    outer_keep_alive_interval = app_config.FAKE_STREAMING_INTERVAL_SECONDS
    if outer_keep_alive_interval > 0:
        while not api_call_task.done():
            keep_alive_data = {"id": "chatcmpl-keepalive", "object": "chat.completion.chunk", "created": int(time.time()), "model": request_obj.model, "choices": [{"delta": {"reasoning_content": ""}, "index": 0, "finish_reason": None}]}
            yield f"data: {json.dumps(keep_alive_data)}\n\n"
            await asyncio.sleep(outer_keep_alive_interval)
    
    try:
        raw_response = await api_call_task # Get the full Gemini response

        # 2. Parse the response for reasoning and content using the centralized parser
        separated_reasoning_text = ""
        separated_actual_content_text = ""
        if hasattr(raw_response, 'candidates') and raw_response.candidates:
            # Typically, fake streaming would focus on the first candidate
            separated_reasoning_text, separated_actual_content_text = parse_gemini_response_for_reasoning_and_content(raw_response.candidates[0])
        elif hasattr(raw_response, 'text') and raw_response.text is not None: # Fallback for simpler response structures
             separated_actual_content_text = raw_response.text


        # 3. Define a text processing function (e.g., for deobfuscation)
        def _process_gemini_text_if_needed(text: str, model_name: str) -> str:
            if model_name.endswith("-encrypt-full"):
                return deobfuscate_text(text)
            return text

        final_reasoning_text = _process_gemini_text_if_needed(separated_reasoning_text, request_obj.model)
        final_actual_content_text = _process_gemini_text_if_needed(separated_actual_content_text, request_obj.model)

        # Define block checking for the raw response
        def _check_gemini_block_wrapper(response_to_check: Any):
            if hasattr(response_to_check, 'prompt_feedback') and hasattr(response_to_check.prompt_feedback, 'block_reason') and response_to_check.prompt_feedback.block_reason:
                block_message = f"Response blocked by Gemini safety filter: {response_to_check.prompt_feedback.block_reason}"
                if hasattr(response_to_check.prompt_feedback, 'block_reason_message') and response_to_check.prompt_feedback.block_reason_message:
                    block_message += f" (Message: {response_to_check.prompt_feedback.block_reason_message})"
                raise ValueError(block_message)

        # Call _base_fake_stream_engine with pre-split and processed texts
        async for chunk in _base_fake_stream_engine(
            api_call_task_creator=lambda: asyncio.create_task(asyncio.sleep(0, result=raw_response)), # Dummy task
            extract_text_from_response_func=lambda r: "", # Not directly used as text is pre-split
            is_valid_response_func=is_gemini_response_valid, # Validates raw_response
            check_block_reason_func=_check_gemini_block_wrapper, # Checks raw_response
            process_text_func=None, # Text processing already done above
            response_id=response_id, 
            sse_model_name=request_obj.model,
            keep_alive_interval_seconds=0, # Keep-alive for this inner call is 0
            is_auto_attempt=is_auto_attempt,
            reasoning_text_to_yield=final_reasoning_text,
            actual_content_text_to_yield=final_actual_content_text
        ):
            yield chunk

    except Exception as e_outer_gemini:
        err_msg_detail = f"Error in gemini_fake_stream_generator (model: '{request_obj.model}'): {type(e_outer_gemini).__name__} - {str(e_outer_gemini)}"
        print(f"ERROR: {err_msg_detail}")
        sse_err_msg_display = str(e_outer_gemini)
        if len(sse_err_msg_display) > 512: sse_err_msg_display = sse_err_msg_display[:512] + "..."
        err_resp_sse = create_openai_error_response(500, sse_err_msg_display, "server_error")
        json_payload_error = json.dumps(err_resp_sse)
        if not is_auto_attempt:
            yield f"data: {json_payload_error}\n\n"
            yield "data: [DONE]\n\n"
        # Consider re-raising if auto-mode needs to catch this: raise e_outer_gemini


async def openai_fake_stream_generator( # Reverted signature: removed thought_tag_marker
    openai_client: AsyncOpenAI,
    openai_params: Dict[str, Any],
    openai_extra_body: Dict[str, Any],
    request_obj: OpenAIRequest,
    is_auto_attempt: bool
    # Removed thought_tag_marker as parsing uses a fixed tag now
    # Removed gcp_credentials, gcp_project_id, gcp_location, base_model_id_for_tokenizer previously
):
    api_model_name = openai_params.get("model", "unknown-openai-model")
    print(f"FAKE STREAMING (OpenAI): Prep for '{request_obj.model}' (API model: '{api_model_name}') with reasoning split.")
    response_id = f"chatcmpl-{int(time.time())}"
    
    async def _openai_api_call_and_split_task_creator_wrapper():
        params_for_non_stream_call = openai_params.copy()
        params_for_non_stream_call['stream'] = False
        
        # Add the tag marker specifically for the internal non-streaming call in fake streaming
        extra_body_for_internal_call = openai_extra_body.copy() # Avoid modifying the original dict
        if 'google' not in extra_body_for_internal_call.get('extra_body', {}):
             if 'extra_body' not in extra_body_for_internal_call: extra_body_for_internal_call['extra_body'] = {}
             extra_body_for_internal_call['extra_body']['google'] = {}
        extra_body_for_internal_call['extra_body']['google']['thought_tag_marker'] = 'vertex_think_tag'
        print("DEBUG: Adding 'thought_tag_marker' for fake-streaming internal call.")

        _api_call_task = asyncio.create_task(
            openai_client.chat.completions.create(**params_for_non_stream_call, extra_body=extra_body_for_internal_call) # Use modified extra_body
        )
        raw_response = await _api_call_task
        full_content_from_api = ""
        if raw_response.choices and raw_response.choices[0].message and raw_response.choices[0].message.content is not None:
            full_content_from_api = raw_response.choices[0].message.content
        vertex_completion_tokens = 0
        if raw_response.usage and raw_response.usage.completion_tokens is not None:
            vertex_completion_tokens = raw_response.usage.completion_tokens
        # --- Start Inserted Block (Tag-based reasoning extraction) ---
        reasoning_text = ""
        # Ensure actual_content_text is a string even if API returns None
        actual_content_text = full_content_from_api if isinstance(full_content_from_api, str) else ""

        fixed_tag = "vertex_think_tag" # Use the fixed tag name
        if actual_content_text: # Check if content exists
            print(f"INFO: OpenAI Direct Fake-Streaming - Applying tag extraction with fixed marker: '{fixed_tag}'")
            # Unconditionally attempt extraction with the fixed tag
            reasoning_text, actual_content_text = extract_reasoning_by_tags(actual_content_text, fixed_tag)
            if reasoning_text:
                 print(f"DEBUG: Tag extraction success (fixed tag). Reasoning len: {len(reasoning_text)}, Content len: {len(actual_content_text)}")
            else:
                 print(f"DEBUG: No content found within fixed tag '{fixed_tag}'.")
        else:
             print(f"WARNING: OpenAI Direct Fake-Streaming - No initial content found in message.")
             actual_content_text = "" # Ensure empty string

        # --- End Revised Block ---

        # The return uses the potentially modified variables:
        return raw_response, reasoning_text, actual_content_text

    temp_task_for_keepalive_check = asyncio.create_task(_openai_api_call_and_split_task_creator_wrapper())
    outer_keep_alive_interval = app_config.FAKE_STREAMING_INTERVAL_SECONDS
    if outer_keep_alive_interval > 0:
        while not temp_task_for_keepalive_check.done():
            keep_alive_data = {"id": "chatcmpl-keepalive", "object": "chat.completion.chunk", "created": int(time.time()), "model": request_obj.model, "choices": [{"delta": {"content": ""}, "index": 0, "finish_reason": None}]}
            yield f"data: {json.dumps(keep_alive_data)}\n\n"
            await asyncio.sleep(outer_keep_alive_interval)

    try:
        full_api_response, separated_reasoning_text, separated_actual_content_text = await temp_task_for_keepalive_check
        def _extract_openai_full_text(response: Any) -> str: 
            if response.choices and response.choices[0].message and response.choices[0].message.content is not None:
                return response.choices[0].message.content
            return ""
        def _is_openai_response_valid(response: Any) -> bool:
            return bool(response.choices and response.choices[0].message is not None)

        async for chunk in _base_fake_stream_engine(
            api_call_task_creator=lambda: asyncio.create_task(asyncio.sleep(0, result=full_api_response)), 
            extract_text_from_response_func=_extract_openai_full_text, 
            is_valid_response_func=_is_openai_response_valid,
            response_id=response_id,
            sse_model_name=request_obj.model,
            keep_alive_interval_seconds=0, 
            is_auto_attempt=is_auto_attempt,
            reasoning_text_to_yield=separated_reasoning_text,
            actual_content_text_to_yield=separated_actual_content_text
        ):
            yield chunk
            
    except Exception as e_outer: 
        err_msg_detail = f"Error in openai_fake_stream_generator outer (model: '{request_obj.model}'): {type(e_outer).__name__} - {str(e_outer)}"
        print(f"ERROR: {err_msg_detail}")
        sse_err_msg_display = str(e_outer)
        if len(sse_err_msg_display) > 512: sse_err_msg_display = sse_err_msg_display[:512] + "..."
        err_resp_sse = create_openai_error_response(500, sse_err_msg_display, "server_error")
        json_payload_error = json.dumps(err_resp_sse)
        if not is_auto_attempt:
            yield f"data: {json_payload_error}\n\n"
            yield "data: [DONE]\n\n"

async def execute_gemini_call(
    current_client: Any, 
    model_to_call: str,  
    prompt_func: Callable[[List[OpenAIMessage]], Union[types.Content, List[types.Content]]], 
    gen_config_for_call: Dict[str, Any], 
    request_obj: OpenAIRequest, 
    is_auto_attempt: bool = False
):
    actual_prompt_for_call = prompt_func(request_obj.messages)
    client_model_name_for_log = getattr(current_client, 'model_name', 'unknown_direct_client_object')
    print(f"INFO: execute_gemini_call for requested API model '{model_to_call}', using client object with internal name '{client_model_name_for_log}'. Original request model: '{request_obj.model}'")

    if request_obj.stream:
        if app_config.FAKE_STREAMING_ENABLED:
            return StreamingResponse(
                gemini_fake_stream_generator( 
                    current_client, 
                    model_to_call, 
                    actual_prompt_for_call, 
                    gen_config_for_call, 
                    request_obj, 
                    is_auto_attempt
                ), 
                media_type="text/event-stream"
            )
        
        response_id_for_stream = f"chatcmpl-{int(time.time())}"
        cand_count_stream = request_obj.n or 1
        
        async def _gemini_real_stream_generator_inner():
            try:
                async for chunk_item_call in await current_client.aio.models.generate_content_stream(
                    model=model_to_call, 
                    contents=actual_prompt_for_call, 
                    config=gen_config_for_call
                ):
                    yield convert_chunk_to_openai(chunk_item_call, request_obj.model, response_id_for_stream, 0)
                yield create_final_chunk(request_obj.model, response_id_for_stream, cand_count_stream)
                yield "data: [DONE]\n\n"
            except Exception as e_stream_call:
                err_msg_detail_stream = f"Streaming Error (Gemini API, model string: '{model_to_call}'): {type(e_stream_call).__name__} - {str(e_stream_call)}"
                print(f"ERROR: {err_msg_detail_stream}")
                s_err = str(e_stream_call); s_err = s_err[:1024]+"..." if len(s_err)>1024 else s_err
                err_resp = create_openai_error_response(500,s_err,"server_error")
                j_err = json.dumps(err_resp)
                if not is_auto_attempt: 
                    yield f"data: {j_err}\n\n"
                    yield "data: [DONE]\n\n"
                raise e_stream_call
        return StreamingResponse(_gemini_real_stream_generator_inner(), media_type="text/event-stream")
    else: 
        response_obj_call = await current_client.aio.models.generate_content(
            model=model_to_call, 
            contents=actual_prompt_for_call, 
            config=gen_config_for_call
        )
        if hasattr(response_obj_call, 'prompt_feedback') and hasattr(response_obj_call.prompt_feedback, 'block_reason') and response_obj_call.prompt_feedback.block_reason:
            block_msg = f"Blocked (Gemini): {response_obj_call.prompt_feedback.block_reason}"
            if hasattr(response_obj_call.prompt_feedback,'block_reason_message') and response_obj_call.prompt_feedback.block_reason_message: 
                block_msg+=f" ({response_obj_call.prompt_feedback.block_reason_message})"
            raise ValueError(block_msg)
        
        if not is_gemini_response_valid(response_obj_call): 
            raise ValueError(f"Invalid non-streaming Gemini response for model string '{model_to_call}'. Resp: {str(response_obj_call)[:200]}")
        return JSONResponse(content=convert_to_openai_format(response_obj_call, request_obj.model))