app.py

import base64 as _b64, json as _j, time as _t, uuid as _u, logging as _l, traceback as _tb, os as _o
from fastapi import FastAPI as _FA, HTTPException as _HE
from fastapi.responses import StreamingResponse as _SR, JSONResponse as _JR
from pydantic import BaseModel as _BM, Field as _F
from typing import List as _L, Optional as _O, Dict as _D, Any as _A, Union as _U
import replicate as _r
from contextlib import asynccontextmanager as _acm

# Obfuscated configuration
_l.basicConfig(level=_l.INFO)
_lg = _l.getLogger(__name__)
_TOKEN = _b64.b64decode(b'cjhfUFpXcWI4WjZxTGQ3NTl3Y2llaG5GR25WYU95MUdEZDFiQkFxUg==').decode('utf-8')

# Supported models configuration
_MODELS = {
    # Anthropic Claude Models
    "claude-4-sonnet": "anthropic/claude-4-sonnet",
    "claude-3.7-sonnet": "anthropic/claude-3.7-sonnet",
    "claude-3.5-sonnet": "anthropic/claude-3.5-sonnet",
    "claude-3.5-haiku": "anthropic/claude-3.5-haiku",

    # OpenAI GPT Models
    "gpt-4.1": "openai/gpt-4.1",
    "gpt-4.1-mini": "openai/gpt-4.1-mini",
    "gpt-4.1-nano": "openai/gpt-4.1-nano",
    "gpt-5": "openai/gpt-5",
    "gpt-5-mini": "openai/gpt-5-mini",
    "gpt-5-nano": "openai/gpt-5-nano",

    # Alternative naming (with provider prefix)
    "anthropic/claude-4-sonnet": "anthropic/claude-4-sonnet",
    "anthropic/claude-3.7-sonnet": "anthropic/claude-3.7-sonnet",
    "anthropic/claude-3.5-sonnet": "anthropic/claude-3.5-sonnet",
    "anthropic/claude-3.5-haiku": "anthropic/claude-3.5-haiku",
    "openai/gpt-4.1": "openai/gpt-4.1",
    "openai/gpt-4.1-mini": "openai/gpt-4.1-mini",
    "openai/gpt-4.1-nano": "openai/gpt-4.1-nano",
    "openai/gpt-5": "openai/gpt-5",
    "openai/gpt-5-mini": "openai/gpt-5-mini",
    "openai/gpt-5-nano": "openai/gpt-5-nano"
}

# Model metadata for OpenAI compatibility
_MODEL_INFO = {
    "claude-4-sonnet": {"owned_by": "anthropic", "context_length": 200000},
    "claude-3.7-sonnet": {"owned_by": "anthropic", "context_length": 200000},
    "claude-3.5-sonnet": {"owned_by": "anthropic", "context_length": 200000},
    "claude-3.5-haiku": {"owned_by": "anthropic", "context_length": 200000},
    "gpt-4.1": {"owned_by": "openai", "context_length": 128000},
    "gpt-4.1-mini": {"owned_by": "openai", "context_length": 128000},
    "gpt-4.1-nano": {"owned_by": "openai", "context_length": 128000},
    "gpt-5": {"owned_by": "openai", "context_length": 400000},
    "gpt-5-mini": {"owned_by": "openai", "context_length": 400000},
    "gpt-5-nano": {"owned_by": "openai", "context_length": 400000}
}

# OpenAI Compatible Models
class _CM(_BM):
    role: str = _F(..., description="Message role")
    content: _O[_U[str, _L[_D[str, _A]]]] = _F(None, description="Message content")
    name: _O[str] = _F(None, description="Message name")
    function_call: _O[_D[str, _A]] = _F(None, description="Function call")
    tool_calls: _O[_L[_D[str, _A]]] = _F(None, description="Tool calls")
    tool_call_id: _O[str] = _F(None, description="Tool call ID")

class _FC(_BM):
    name: str = _F(..., description="Function name")
    arguments: str = _F(..., description="Function arguments")

class _TC(_BM):
    id: str = _F(..., description="Tool call ID")
    type: str = _F(default="function", description="Tool call type")
    function: _FC = _F(..., description="Function call")

class _FD(_BM):
    name: str = _F(..., description="Function name")
    description: _O[str] = _F(None, description="Function description")
    parameters: _D[str, _A] = _F(..., description="Function parameters")

class _TD(_BM):
    type: str = _F(default="function", description="Tool type")
    function: _FD = _F(..., description="Function definition")

class _CCR(_BM):
    model: str = _F(..., description="Model name")
    messages: _L[_CM] = _F(..., description="Messages")
    max_tokens: _O[int] = _F(default=4096, description="Max tokens")
    temperature: _O[float] = _F(default=0.7, description="Temperature")
    top_p: _O[float] = _F(default=1.0, description="Top p")
    n: _O[int] = _F(default=1, description="Number of completions")
    stream: _O[bool] = _F(default=False, description="Stream response")
    stop: _O[_U[str, _L[str]]] = _F(None, description="Stop sequences")
    presence_penalty: _O[float] = _F(default=0.0, description="Presence penalty")
    frequency_penalty: _O[float] = _F(default=0.0, description="Frequency penalty")
    logit_bias: _O[_D[str, float]] = _F(None, description="Logit bias")
    user: _O[str] = _F(None, description="User ID")
    tools: _O[_L[_TD]] = _F(None, description="Available tools")
    tool_choice: _O[_U[str, _D[str, _A]]] = _F(None, description="Tool choice")
    functions: _O[_L[_FD]] = _F(None, description="Available functions")
    function_call: _O[_U[str, _D[str, _A]]] = _F(None, description="Function call")

class _CCC(_BM):
    index: int = _F(default=0, description="Choice index")
    message: _CM = _F(..., description="Message")
    finish_reason: _O[str] = _F(None, description="Finish reason")

class _CCSC(_BM):
    index: int = _F(default=0, description="Choice index")
    delta: _D[str, _A] = _F(..., description="Delta")
    finish_reason: _O[str] = _F(None, description="Finish reason")

class _CCRes(_BM):
    id: str = _F(..., description="Completion ID")
    object: str = _F(default="chat.completion", description="Object type")
    created: int = _F(..., description="Created timestamp")
    model: str = _F(..., description="Model name")
    choices: _L[_CCC] = _F(..., description="Choices")
    usage: _D[str, int] = _F(..., description="Usage stats")
    system_fingerprint: _O[str] = _F(None, description="System fingerprint")

class _CCSR(_BM):
    id: str = _F(..., description="Completion ID")
    object: str = _F(default="chat.completion.chunk", description="Object type")
    created: int = _F(..., description="Created timestamp")
    model: str = _F(..., description="Model name")
    choices: _L[_CCSC] = _F(..., description="Choices")
    system_fingerprint: _O[str] = _F(None, description="System fingerprint")

class _OM(_BM):
    id: str = _F(..., description="Model ID")
    object: str = _F(default="model", description="Object type")
    created: int = _F(..., description="Created timestamp")
    owned_by: str = _F(..., description="Owner")

# Replicate Client
class _RC:
    def __init__(self, _tk=_TOKEN):
        _o.environ['REPLICATE_API_TOKEN'] = _tk
        self._client = _r
        self._models = _MODELS
        self._model_info = _MODEL_INFO

    def _get_replicate_model(self, _model_name):
        """Get the Replicate model ID from OpenAI model name"""
        return self._models.get(_model_name, _model_name)

    def _validate_model(self, _model_name):
        """Validate if model is supported"""
        return _model_name in self._models or _model_name in self._models.values()

    def _format_messages(self, _msgs):
        _prompt = ""
        _system = ""

        for _msg in _msgs:
            _role = _msg.get('role', '')
            _content = _msg.get('content', '')

            if _role == 'system':
                _system = _content
            elif _role == 'user':
                _prompt += f"Human: {_content}\n\n"
            elif _role == 'assistant':
                _prompt += f"Assistant: {_content}\n\n"

        _prompt += "Assistant: "
        return _prompt, _system

    def _sanitize_params(self, **_kwargs):
        """Sanitize parameters and set proper defaults"""
        _params = {}

        # Handle max_tokens
        _max_tokens = _kwargs.get('max_tokens')
        if _max_tokens is not None and _max_tokens > 0:
            # Replicate Anthropic models often require >= 1024; clamp to avoid 422s
            try:
                _mt = int(_max_tokens)
            except Exception:
                _mt = 4096
            _params['max_tokens'] = max(1024, _mt)
        else:
            _params['max_tokens'] = 4096

        # Handle temperature
        _temperature = _kwargs.get('temperature')
        if _temperature is not None:
            _params['temperature'] = max(0.0, min(2.0, float(_temperature)))
        else:
            _params['temperature'] = 0.7

        # Handle top_p
        _top_p = _kwargs.get('top_p')
        if _top_p is not None:
            _params['top_p'] = max(0.0, min(1.0, float(_top_p)))
        else:
            _params['top_p'] = 1.0

        # Handle presence_penalty
        _presence_penalty = _kwargs.get('presence_penalty')
        if _presence_penalty is not None:
            _params['presence_penalty'] = max(-2.0, min(2.0, float(_presence_penalty)))
        else:
            _params['presence_penalty'] = 0.0

        # Handle frequency_penalty
        _frequency_penalty = _kwargs.get('frequency_penalty')
        if _frequency_penalty is not None:
            _params['frequency_penalty'] = max(-2.0, min(2.0, float(_frequency_penalty)))
        else:
            _params['frequency_penalty'] = 0.0

        return _params

    def _create_prediction(self, _model_name, _prompt, _system="", **_kwargs):
        """Create a prediction using Replicate API"""
        _replicate_model = self._get_replicate_model(_model_name)
        _params = self._sanitize_params(**_kwargs)

        _input = {
            "prompt": _prompt,
            "system_prompt": _system,
            "max_tokens": _params['max_tokens'],
            "temperature": _params['temperature'],
            "top_p": _params['top_p']
        }

        try:
            _prediction = self._client.predictions.create(
                model=_replicate_model,
                input=_input
            )
            return _prediction
        except Exception as _e:
            _lg.error(f"Prediction creation error for {_replicate_model}: {_e}")
            return None
        
    def _handle_tools(self, _tools, _tool_choice):
        if not _tools:
            return ""
            
        _tool_prompt = "\n\nYou have access to the following tools:\n"
        for _tool in _tools:
            _func = _tool.get('function', {})
            _name = _func.get('name', '')
            _desc = _func.get('description', '')
            _params = _func.get('parameters', {})
            _tool_prompt += f"- {_name}: {_desc}\n"
            _tool_prompt += f"  Parameters: {_j.dumps(_params)}\n"
            
        _tool_prompt += "\nTo use a tool, respond with JSON in this format:\n"
        _tool_prompt += '{"tool_calls": [{"id": "call_123", "type": "function", "function": {"name": "tool_name", "arguments": "{\\"param\\": \\"value\\"}"}}]}\n'
        
        return _tool_prompt
        
    def _stream_chat(self, _model_name, _prompt, _system="", **_kwargs):
        """Stream chat using Replicate's streaming API, yielding only text chunks."""
        _replicate_model = self._get_replicate_model(_model_name)
        _params = self._sanitize_params(**_kwargs)

        _input = {
            "prompt": _prompt,
            "system_prompt": _system,
            "max_tokens": _params['max_tokens'],
            "temperature": _params['temperature'],
            "top_p": _params['top_p']
        }

        # pass through stop sequences if provided
        if 'stop' in _kwargs and _kwargs['stop'] is not None:
            _input["stop"] = _kwargs['stop']

        try:
            for _event in self._client.stream(_replicate_model, input=_input):
                if not _event:
                    continue

                # Fast path: plain string/bytes token
                if isinstance(_event, (str, bytes)):
                    yield (_event.decode('utf-8', errors='ignore') if isinstance(_event, bytes) else _event)
                    continue

                # Normalize event interfaces (object, dict, or custom)
                _etype, _edata = None, None
                if isinstance(_event, dict):
                    _etype = _event.get('type') or _event.get('event')
                    _edata = _event.get('data') or _event.get('output') or _event.get('text')
                else:
                    _etype = getattr(_event, 'type', None) or getattr(_event, 'event', None)
                    _edata = getattr(_event, 'data', None)

                # Extract text payloads
                if _etype == "output" or _edata is not None:
                    if isinstance(_edata, (list, tuple)):
                        for _piece in _edata:
                            if isinstance(_piece, (str, bytes)):
                                yield (_piece.decode('utf-8', errors='ignore') if isinstance(_piece, bytes) else _piece)
                    elif isinstance(_edata, (str, bytes)):
                        yield (_edata.decode('utf-8', errors='ignore') if isinstance(_edata, bytes) else _edata)
                    elif isinstance(_edata, dict):
                        # Common nested keys
                        for _k in ("text", "output", "delta"):
                            if _k in _edata and isinstance(_edata[_k], (str, bytes)):
                                _v = _edata[_k]
                                yield (_v.decode('utf-8', errors='ignore') if isinstance(_v, bytes) else _v)
                                break
                    elif _etype in {"completed", "done", "end"}:
                        break
                    else:
                        # Fallback to string form (restore old working behavior)
                        try:
                            _s = str(_event)
                            if _s:
                                yield _s
                        except Exception:
                            pass
                elif _etype in {"error", "logs", "warning"}:
                    try:
                        _lg.warning(f"Replicate stream {_etype}: {_edata}")
                    except Exception:
                        pass
                elif _etype in {"completed", "done", "end"}:
                    break
                else:
                    # Unknown/eventless object; fallback to string form
                    try:
                        _s = str(_event)
                        if _s:
                            yield _s
                    except Exception:
                        pass
        except Exception as _e:
            _lg.error(f"Streaming error for {_replicate_model}: {_e}")
            # Surface a minimal safe error token
            yield ""

    def _stream_from_prediction(self, _prediction):
        """Stream from a prediction using the stream URL"""
        try:
            import requests
            _stream_url = _prediction.urls.get('stream')
            if not _stream_url:
                _lg.error("No stream URL available")
                return

            _response = requests.get(
                _stream_url,
                headers={
                    "Accept": "text/event-stream",
                    "Cache-Control": "no-store"
                },
                stream=True
            )

            for _line in _response.iter_lines():
                if _line:
                    _line = _line.decode('utf-8')
                    if _line.startswith('data: '):
                        _data = _line[6:]
                        if _data != '[DONE]':
                            yield _data
                        else:
                            break

        except Exception as _e:
            _lg.error(f"Stream from prediction error: {_e}")
            yield f"Error: {_e}"
            
    def _complete_chat(self, _model_name, _prompt, _system="", **_kwargs):
        """Complete chat using Replicate's run method and coalesce into a single string."""
        _replicate_model = self._get_replicate_model(_model_name)
        _params = self._sanitize_params(**_kwargs)

        _input = {
            "prompt": _prompt,
            "system_prompt": _system,
            "max_tokens": _params['max_tokens'],
            "temperature": _params['temperature'],
            "top_p": _params['top_p']
        }

        if 'stop' in _kwargs and _kwargs['stop'] is not None:
            _input["stop"] = _kwargs['stop']

        try:
            _result = self._client.run(_replicate_model, input=_input)

            # If it's a list of strings or chunks, join
            if isinstance(_result, list):
                _joined = "".join([x.decode("utf-8", errors="ignore") if isinstance(x, bytes) else str(x) for x in _result])
                return _joined

            # Some models return generators/iterables; accumulate
            try:
                from collections.abc import Iterator, Iterable
                if isinstance(_result, Iterator) or (
                    isinstance(_result, Iterable) and not isinstance(_result, (str, bytes))
                ):
                    _buf = []
                    for _piece in _result:
                        if isinstance(_piece, (str, bytes)):
                            _buf.append(_piece.decode("utf-8", errors="ignore") if isinstance(_piece, bytes) else _piece)
                        else:
                            _buf.append(str(_piece))
                    _text = "".join(_buf)
                    if _text:
                        return _text
            except Exception:
                pass

            # FileOutput or scalar: cast to string; if empty, safe fallback
            _text = str(_result) if _result is not None else ""
            return _text
        except Exception as _e:
            _lg.error(f"Completion error for {_replicate_model}: {_e}")
            # Return empty to avoid leaking internals into user-visible content
            return ""

# Global variables
_client = None
_startup_time = _t.time()
_request_count = 0
_error_count = 0

@_acm
async def _lifespan(_app: _FA):
    global _client
    try:
        _lg.info("Initializing Replicate client...")
        _client = _RC()
        _lg.info("Replicate client initialized successfully")
    except Exception as _e:
        _lg.error(f"Failed to initialize client: {_e}")
        _client = None
    
    yield
    _lg.info("Shutting down Replicate client...")

# FastAPI App
_app = _FA(
    title="Replicate Claude-4-Sonnet OpenAI API", 
    version="1.0.0",
    description="OpenAI-compatible API for Claude-4-Sonnet via Replicate",
    lifespan=_lifespan
)

# CORS
try:
    from fastapi.middleware.cors import CORSMiddleware as _CORS
    _app.add_middleware(
        _CORS,
        allow_origins=["*"],
        allow_credentials=True,
        allow_methods=["*"],
        allow_headers=["*"],
    )
except ImportError:
    pass

# Error handlers
@_app.exception_handler(_HE)
async def _http_exception_handler(_request, _exc: _HE):
    _lg.error(f"HTTP error: {_exc.status_code} - {_exc.detail}")
    return _JR(
        status_code=_exc.status_code,
        content={
            "error": {
                "message": _exc.detail,
                "type": "api_error",
                "code": _exc.status_code
            }
        }
    )

@_app.exception_handler(Exception)
async def _global_exception_handler(_request, _exc):
    _lg.error(f"Unexpected error: {_exc}\n{_tb.format_exc()}")
    return _JR(
        status_code=500,
        content={
            "error": {
                "message": "Internal server error",
                "type": "server_error",
                "code": 500
            }
        }
    )

@_app.get("/")
async def _root():
    _model_count = len([m for m in _MODELS.keys() if not m.startswith(('anthropic/', 'openai/'))])
    return {
        "message": "Replicate Multi-Model OpenAI API",
        "version": "1.0.0",
        "status": "running",
        "supported_models": _model_count,
        "providers": ["anthropic", "openai"]
    }

@_app.get("/health")
async def _health_check():
    global _client, _startup_time, _request_count, _error_count
    
    _uptime = _t.time() - _startup_time
    _status = "healthy"
    
    _client_status = "unknown"
    if _client is None:
        _client_status = "not_initialized"
        _status = "degraded"
    else:
        _client_status = "ready"
    
    return {
        "status": _status,
        "timestamp": int(_t.time()),
        "uptime_seconds": int(_uptime),
        "client_status": _client_status,
        "stats": {
            "total_requests": _request_count,
            "total_errors": _error_count,
            "error_rate": _error_count / max(_request_count, 1)
        }
    }

@_app.get("/v1/models")
async def _list_models():
    """List all supported models"""
    _models_list = []
    _created_time = int(_t.time())

    # Get unique model names (remove duplicates from alternative naming)
    _unique_models = set()
    for _model_name in _MODELS.keys():
        if not _model_name.startswith(('anthropic/', 'openai/')):
            _unique_models.add(_model_name)

    # Create model objects
    for _model_name in sorted(_unique_models):
        _info = _MODEL_INFO.get(_model_name, {"owned_by": "unknown", "context_length": 4096})
        _models_list.append(_OM(
            id=_model_name,
            created=_created_time,
            owned_by=_info["owned_by"]
        ))

    return {
        "object": "list",
        "data": _models_list
    }

@_app.get("/models")
async def _list_models_alt():
    return await _list_models()

async def _generate_stream_response(_request: _CCR, _prompt: str, _system: str, _request_id: str = None):
    _completion_id = f"chatcmpl-{_u.uuid4().hex}"
    _created_time = int(_t.time())
    _request_id = _request_id or f"req-{_u.uuid4().hex[:8]}"
    
    _lg.info(f"[{_request_id}] Starting stream generation")

    try:
        # Send initial chunk with role
        _initial_chunk = {
            "id": _completion_id,
            "object": "chat.completion.chunk",
            "created": _created_time,
            "model": _request.model,
            "choices": [{
                "index": 0,
                "delta": {"role": "assistant"},
                "finish_reason": None
            }]
        }
        yield f"data: {_j.dumps(_initial_chunk)}\n\n"

        # Stream content chunks using Replicate's streaming
        _chunk_count = 0
        _total_content = ""

        try:
            # Extract only relevant parameters for Replicate API
            _api_params = {
                'max_tokens': _request.max_tokens,
                'temperature': _request.temperature,
                'top_p': _request.top_p,
                'presence_penalty': _request.presence_penalty,
                'frequency_penalty': _request.frequency_penalty,
                'stop': _request.stop
            }

            # Use Replicate's direct streaming method with model parameter
            for _chunk in _client._stream_chat(_request.model, _prompt, _system, **_api_params):
                if _chunk and isinstance(_chunk, str):
                    _chunk_count += 1
                    _total_content += _chunk

                    _stream_response = _CCSR(
                        id=_completion_id,
                        created=_created_time,
                        model=_request.model,
                        choices=[_CCSC(
                            delta={"content": _chunk},
                            finish_reason=None
                        )]
                    )

                    try:
                        _chunk_json = _j.dumps(_stream_response.model_dump())
                        yield f"data: {_chunk_json}\n\n"
                    except Exception as _json_error:
                        _lg.error(f"[{_request_id}] JSON serialization error: {_json_error}")
                        continue
                        
        except Exception as _stream_error:
            _lg.error(f"[{_request_id}] Streaming error after {_chunk_count} chunks: {_stream_error}")
            
            if _chunk_count == 0:
                _error_content = "I apologize, but I encountered an error while generating the response. Please try again."
                _error_response = _CCSR(
                    id=_completion_id,
                    created=_created_time,
                    model=_request.model,
                    choices=[_CCSC(
                        delta={"content": _error_content},
                        finish_reason=None
                    )]
                )
                yield f"data: {_j.dumps(_error_response.model_dump())}\n\n"

        _lg.info(f"[{_request_id}] Stream completed: {_chunk_count} chunks, {len(_total_content)} characters")

    except Exception as _e:
        _lg.error(f"[{_request_id}] Critical streaming error: {_e}")
        _error_chunk = {
            "id": _completion_id,
            "object": "chat.completion.chunk",
            "created": _created_time,
            "model": _request.model,
            "choices": [{
                "index": 0,
                "delta": {"content": "Error occurred while streaming response."},
                "finish_reason": "stop"
            }]
        }
        yield f"data: {_j.dumps(_error_chunk)}\n\n"

    finally:
        try:
            _final_chunk = {
                "id": _completion_id,
                "object": "chat.completion.chunk",
                "created": _created_time,
                "model": _request.model,
                "choices": [{
                    "index": 0,
                    "delta": {},
                    "finish_reason": "stop"
                }]
            }
            yield f"data: {_j.dumps(_final_chunk)}\n\n"
            yield "data: [DONE]\n\n"
            _lg.info(f"[{_request_id}] Stream finalized")
        except Exception as _final_error:
            _lg.error(f"[{_request_id}] Error sending final chunk: {_final_error}")
            yield "data: [DONE]\n\n"

@_app.post("/v1/chat/completions")
async def _create_chat_completion(_request: _CCR):
    global _request_count, _error_count, _client

    _request_count += 1
    _request_id = f"req-{_u.uuid4().hex[:8]}"
    _lg.info(f"[{_request_id}] Chat completion request: model={_request.model}, stream={_request.stream}")

    if _client is None:
        _error_count += 1
        _lg.error(f"[{_request_id}] Client not initialized")
        raise _HE(status_code=503, detail="Service temporarily unavailable")

    try:
        # Validate model
        if not _client._validate_model(_request.model):
            _supported_models = list(_MODELS.keys())
            raise _HE(status_code=400, detail=f"Model '{_request.model}' not supported. Supported models: {_supported_models}")

        # Format messages
        _prompt, _system = _client._format_messages([_msg.model_dump() for _msg in _request.messages])

        # Handle tools/functions
        if _request.tools or _request.functions:
            _tools = _request.tools or [_TD(function=_func) for _func in (_request.functions or [])]
            _tool_prompt = _client._handle_tools([_tool.model_dump() for _tool in _tools], _request.tool_choice)
            _prompt += _tool_prompt

        _lg.info(f"[{_request_id}] Formatted prompt length: {len(_prompt)}")

        # Extract only relevant parameters for Replicate API
        _api_params = {
            'max_tokens': _request.max_tokens,
            'temperature': _request.temperature,
            'top_p': _request.top_p,
            'presence_penalty': _request.presence_penalty,
            'frequency_penalty': _request.frequency_penalty
        }

        _lg.info(f"[{_request_id}] API parameters: {_api_params}")

        # Stream or complete
        if _request.stream:
            _lg.info(f"[{_request_id}] Starting streaming response")
            return _SR(
                _generate_stream_response(_request, _prompt, _system, _request_id),
                media_type="text/event-stream",
                headers={
                    "Cache-Control": "no-cache",
                    "Connection": "keep-alive"
                }
            )
        else:
            # Non-streaming completion
            _lg.info(f"[{_request_id}] Starting non-streaming completion")
            _content = _client._complete_chat(_request.model, _prompt, _system, **_api_params)

            _completion_id = f"chatcmpl-{_u.uuid4().hex}"
            _created_time = int(_t.time())

            # Check for tool calls in response
            _tool_calls = None
            _finish_reason = "stop"

            try:
                if _content.strip().startswith('{"tool_calls"'):
                    _tool_data = _j.loads(_content.strip())
                    if "tool_calls" in _tool_data:
                        _tool_calls = [_TC(**_tc) for _tc in _tool_data["tool_calls"]]
                        _finish_reason = "tool_calls"
                        _content = None
            except:
                pass

            _response = _CCRes(
                id=_completion_id,
                created=_created_time,
                model=_request.model,
                choices=[_CCC(
                    message=_CM(
                        role="assistant",
                        content=_content,
                        tool_calls=[_tc.model_dump() for _tc in _tool_calls] if _tool_calls else None
                    ),
                    finish_reason=_finish_reason
                )],
                usage={
                    "prompt_tokens": len(_prompt.split()),
                    "completion_tokens": len(_content.split()) if _content else 0,
                    "total_tokens": len(_prompt.split()) + (len(_content.split()) if _content else 0)
                }
            )

            _lg.info(f"[{_request_id}] Non-streaming completion finished")
            return _response

    except _HE:
        _error_count += 1
        raise
    except Exception as _e:
        _error_count += 1
        _lg.error(f"[{_request_id}] Unexpected error: {_e}\n{_tb.format_exc()}")
        raise _HE(status_code=500, detail="Internal server error occurred")

@_app.post("/chat/completions")
async def _create_chat_completion_alt(_request: _CCR):
    return await _create_chat_completion(_request)

if __name__ == "__main__":
    try:
        import uvicorn as _uv
        _port = int(_o.getenv("PORT", 7860))  # Hugging Face default port
        _host = _o.getenv("HOST", "0.0.0.0")

        _lg.info(f"Starting Replicate Multi-Model server on {_host}:{_port}")
        _lg.info(f"Supported models: {list(_MODELS.keys())[:7]}")  # Show first 7 models
        _uv.run(
            _app,
            host=_host,
            port=_port,
            reload=False,
            log_level="info",
            access_log=True
        )
    except ImportError:
        _lg.error("uvicorn not installed. Install with: pip install uvicorn")
    except Exception as _e:
        _lg.error(f"Failed to start server: {_e}")