main.py

import base64
import json
import os
import secrets
import string
import time
from typing import List, Optional, Union, Any
import httpx
from dotenv import load_dotenv
from fastapi import FastAPI
from fastapi.responses import JSONResponse, StreamingResponse
from pydantic import BaseModel

# --- Configuration ---
load_dotenv()
# Env variables for external services
IMAGE_API_URL = os.environ.get("IMAGE_API_URL", "https://image.api.example.com")
SNAPZION_UPLOAD_URL = "https://upload.snapzion.com/api/public-upload"
SNAPZION_API_KEY = os.environ.get("SNAP", "")

# --- Dummy Model Definitions ---
# In a real application, these would be defined properly.
AVAILABLE_MODELS = [
    {"id": "gpt-4-turbo", "object": "model", "created": int(time.time()), "owned_by": "system"},
    {"id": "gpt-4o", "object": "model", "created": int(time.time()), "owned_by": "system"},
    {"id": "gpt-3.5-turbo", "object": "model", "created": int(time.time()), "owned_by": "system"},
    {"id": "dall-e-3", "object": "model", "created": int(time.time()), "owned_by": "system"},
    {"id": "text-moderation-stable", "object": "model", "created": int(time.time()), "owned_by": "system"},
]
MODEL_ALIASES = {}

# --- FastAPI Application ---
app = FastAPI(
    title="OpenAI Compatible API",
    description="An adapter for various services to be compatible with the OpenAI API specification.",
    version="1.0.0"
)

# --- Helper Function for Random ID Generation ---
def generate_random_id(prefix: str, length: int = 29) -> str:
    """
    Generates a cryptographically secure, random alphanumeric ID.
    """
    population = string.ascii_letters + string.digits
    random_part = "".join(secrets.choice(population) for _ in range(length))
    return f"{prefix}{random_part}"

# === API Endpoints ===
@app.get("/v1/models")
async def list_models():
    """Lists the available models."""
    return {"object": "list", "data": AVAILABLE_MODELS}

# === Chat Completion ===
class Message(BaseModel):
    role: str
    content: str

class ChatRequest(BaseModel):
    messages: List[Message]
    model: str
    stream: Optional[bool] = False
    tools: Optional[Any] = None

@app.post("/v1/chat/completions")
async def chat_completion(request: ChatRequest):
    """
    Handles chat completion requests, supporting both streaming and non-streaming responses.
    """
    model_id = MODEL_ALIASES.get(request.model, request.model)
    chat_id = generate_random_id("chatcmpl-")
    headers = {
        'accept': 'text/event-stream',
        'content-type': 'application/json',
        'origin': 'https://www.chatwithmono.xyz',
        'referer': 'https://www.chatwithmono.xyz/',
        'user-agent': 'Mozilla/5.0',
    }
    if request.tools:
        # Handle tool by giving in system prompt.
        # Tool call must be encoded in <tool_call><tool_call> XML tag.
        tool_prompt = f"""You have access to the following tools . To call a tool, please respond with JSON for a tool call within <tool_call><tool_call> XML tag. Respond in the format {{"name": tool name, "parameters": dictionary of argument name and its value}}. Do not use variables.
Tools:
{";".join(f"<tool>{tool}</tool>" for tool in request.tools)}

Response Format for tool call:
For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{{"name": <function-name>, "arguments": <args-json-object>}}
</tool_call>

Example of tool calling:
<tool_call>
{{"name": "get_weather", "parameters": {{"city": "New York"}}}}
</tool_call>

Using tools is recommended.
        """
        if request.messages[0].role == "system":
            request.messages[0].content += "\n\n" + tool_prompt
        else:
            request.messages.insert(0, {"role": "system", "content": tool_prompt})
    request_data = request.model_dump(exclude_unset=True)

    payload = {
        "messages": request_data["messages"],
        "model": model_id
    }
    if request.stream:
        async def event_stream():
            created = int(time.time())
            is_first_chunk = True
            usage_info = None
            is_tool_call = False
            chunks_buffer = []
            max_initial_chunks = 4  # Number of initial chunks to buffer
            try:
                async with httpx.AsyncClient(timeout=120) as client:
                    async with client.stream("POST", "https://www.chatwithmono.xyz/api/chat", headers=headers, json=payload) as response:
                        response.raise_for_status()
                        async for line in response.aiter_lines():
                            if not line: continue
                            if line.startswith("0:"):
                                try:
                                    content_piece = json.loads(line[2:])
                                    print(content_piece)
                                    # Buffer the first few chunks
                                    if len(chunks_buffer) < max_initial_chunks:
                                        chunks_buffer.append(content_piece)
                                        continue
                                    # Process the buffered chunks if we haven't already
                                    if chunks_buffer and not is_tool_call:
                                        full_buffer = ''.join(chunks_buffer)
                                        if "<tool_call>" in full_buffer:
                                            print("Tool call detected")
                                            is_tool_call = True

                                    # Process the current chunk
                                    if is_tool_call:
                                        chunks_buffer.append(content_piece)

                                        full_buffer = ''.join(chunks_buffer)

                                        if "</tool_call>" in full_buffer:
                                            print("Tool call End detected")
                                            # Process tool call in the current chunk
                                            tool_call_str = full_buffer.split("<tool_call>")[1].split("</tool_call>")[0]
                                            tool_call_json = json.loads(tool_call_str.strip())
                                            delta = {
                                                "content": None,
                                                "tool_calls": [{
                                                    "index": 0,
                                                    "id": generate_random_id("call_"),
                                                    "type": "function",
                                                    "function": {
                                                        "name": tool_call_json["name"],
                                                        "arguments": json.dumps(tool_call_json["parameters"])
                                                    }
                                                }]
                                            }
                                            chunk_data = {
                                                "id": chat_id, "object": "chat.completion.chunk", "created": created,
                                                "model": model_id,
                                                "choices": [{"index": 0, "delta": delta, "finish_reason": None}],
                                                "usage": None
                                            }
                                            yield f"data: {json.dumps(chunk_data)}\n\n"
                                        else:
                                            continue
                                    else:

                                        # Regular content
                                        if is_first_chunk:
                                            delta = {"content": "".join(chunks_buffer), "tool_calls": None}
                                            delta["role"] = "assistant"
                                            is_first_chunk = False
                                            chunk_data = {
                                            "id": chat_id, "object": "chat.completion.chunk", "created": created,
                                            "model": model_id,
                                            "choices": [{"index": 0, "delta": delta, "finish_reason": None}],
                                            "usage": None
                                            }
                                            yield f"data: {json.dumps(chunk_data)}\n\n"

                                        delta = {"content": content_piece, "tool_calls": None}

                                        chunk_data = {
                                            "id": chat_id, "object": "chat.completion.chunk", "created": created,
                                            "model": model_id,
                                            "choices": [{"index": 0, "delta": delta, "finish_reason": None}],
                                            "usage": None
                                        }
                                        yield f"data: {json.dumps(chunk_data)}\n\n"
                                except json.JSONDecodeError: continue
                            elif line.startswith(("e:", "d:")):
                                try:
                                    usage_info = json.loads(line[2:]).get("usage")
                                except (json.JSONDecodeError, AttributeError): pass
                                break

                        final_usage = None
                        if usage_info:
                            prompt_tokens = usage_info.get("promptTokens", 0)
                            completion_tokens = usage_info.get("completionTokens", 0)
                            final_usage = {
                                "prompt_tokens": prompt_tokens, "completion_tokens": completion_tokens,
                                "total_tokens": prompt_tokens + completion_tokens,
                            }
                        done_chunk = {
                            "id": chat_id, "object": "chat.completion.chunk", "created": created, "model": model_id,
                            "choices": [{
                                "index": 0,
                                "delta": {"role": "assistant", "content": None, "function_call": None, "tool_calls": None},
                                "finish_reason": "stop"
                            }],
                            "usage": final_usage
                        }
                        yield f"data: {json.dumps(done_chunk)}\n\n"
            except httpx.HTTPStatusError as e:
                error_content = {
                    "error": {
                        "message": f"Upstream API error: {e.response.status_code}. Details: {e.response.text}",
                        "type": "upstream_error", "code": str(e.response.status_code)
                    }
                }
                yield f"data: {json.dumps(error_content)}\n\n"
            finally:
                yield "data: [DONE]\n\n"
        return StreamingResponse(event_stream(), media_type="text/event-stream")
    else: # Non-streaming
        assistant_response, usage_info = "", {}
        tool_call_json = None
        try:
            async with httpx.AsyncClient(timeout=120) as client:
                async with client.stream("POST", "https://www.chatwithmono.xyz/api/chat", headers=headers, json=payload) as response:
                    response.raise_for_status()
                    async for chunk in response.aiter_lines():
                        if chunk.startswith("0:"):
                            try: assistant_response += json.loads(chunk[2:])
                            except: continue
                        elif chunk.startswith(("e:", "d:")):
                            try: usage_info = json.loads(chunk[2:]).get("usage", {})
                            except: continue

            if "<tool_call>" in assistant_response and "</tool_call>" in assistant_response:
                tool_call_str = assistant_response.split("<tool_call>")[1].split("</tool_call>")[0]
                tool_call = json.loads(tool_call_str.strip())
                tool_call_json = [{"id": generate_random_id("call_"),"function": {"name": tool_call["name"], "arguments": json.dumps(tool_call["parameters"])}}]


                
            return JSONResponse(content={
                "id": chat_id, "object": "chat.completion", "created": int(time.time()), "model": model_id,
                "choices": [{"index": 0, "message": {"role": "assistant", "content": assistant_response if tool_call_json is None else None, "tool_calls": tool_call_json}, "finish_reason": "stop"}],
                "usage": {
                    "prompt_tokens": usage_info.get("promptTokens", 0),
                    "completion_tokens": usage_info.get("completionTokens", 0),
                    "total_tokens": usage_info.get("promptTokens", 0) + usage_info.get("completionTokens", 0),
                }
            })
        except httpx.HTTPStatusError as e:
            return JSONResponse(status_code=e.response.status_code, content={"error": {"message": f"Upstream API error. Details: {e.response.text}", "type": "upstream_error"}})


# === Image Generation ===
class ImageGenerationRequest(BaseModel):
    prompt: str
    aspect_ratio: Optional[str] = "1:1"
    n: Optional[int] = 1
    user: Optional[str] = None
    model: Optional[str] = "default"

@app.post("/v1/images/generations")
async def generate_images(request: ImageGenerationRequest):
    """Handles image generation requests."""
    results = []
    try:
        async with httpx.AsyncClient(timeout=120) as client:
            for _ in range(request.n):
                model = request.model or "default"
                if model in ["gpt-image-1", "dall-e-3", "dall-e-2", "nextlm-image-1"]:
                    headers = {'Content-Type': 'application/json', 'User-Agent': 'Mozilla/5.0', 'Referer': 'https://www.chatwithmono.xyz/'}
                    payload = {"prompt": request.prompt, "model": model}
                    resp = await client.post("https://www.chatwithmono.xyz/api/image", headers=headers, json=payload)
                    resp.raise_for_status()
                    data = resp.json()
                    b64_image = data.get("image")
                    if not b64_image: return JSONResponse(status_code=502, content={"error": "Missing base64 image in response"})
                    if SNAPZION_API_KEY:
                        upload_headers = {"Authorization": SNAPZION_API_KEY}
                        upload_files = {'file': ('image.png', base64.b64decode(b64_image), 'image/png')}
                        upload_resp = await client.post(SNAPZION_UPLOAD_URL, headers=upload_headers, files=upload_files)
                        upload_resp.raise_for_status()
                        upload_data = upload_resp.json()
                        image_url = upload_data.get("url")
                    else:
                        image_url = f"data:image/png;base64,{b64_image}"
                    results.append({"url": image_url, "b64_json": b64_image, "revised_prompt": data.get("revised_prompt")})
                else:
                    params = {"prompt": request.prompt, "aspect_ratio": request.aspect_ratio, "link": "typegpt.net"}
                    resp = await client.get(IMAGE_API_URL, params=params)
                    resp.raise_for_status()
                    data = resp.json()
                    results.append({"url": data.get("image_link"), "b64_json": data.get("base64_output")})
    except httpx.HTTPStatusError as e:
        return JSONResponse(status_code=502, content={"error": f"Image generation failed. Upstream error: {e.response.status_code}", "details": e.response.text})
    except Exception as e:
        return JSONResponse(status_code=500, content={"error": "An internal error occurred.", "details": str(e)})
    return {"created": int(time.time()), "data": results}

# === Moderation Endpoint ===
class ModerationRequest(BaseModel):
    input: Union[str, List[str]]
    model: Optional[str] = "text-moderation-stable"

@app.post("/v1/moderations")
async def create_moderation(request: ModerationRequest):
    """
    Handles moderation requests, conforming to the OpenAI API specification.
    Includes a custom 'reason' field in the result if provided by the upstream API.
    """
    input_texts = [request.input] if isinstance(request.input, str) else request.input
    if not input_texts:
         return JSONResponse(status_code=400, content={"error": {"message": "Request must have at least one input string.", "type": "invalid_request_error"}})
    moderation_url = "https://www.chatwithmono.xyz/api/moderation"
    headers = {
        'Content-Type': 'application/json',
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/138.0.0.0 Safari/537.36',
        'Referer': 'https://www.chatwithmono.xyz/',
    }
    results = []
    try:
        async with httpx.AsyncClient(timeout=30) as client:
            for text_input in input_texts:
                payload = {"text": text_input}
                resp = await client.post(moderation_url, headers=headers, json=payload)
                resp.raise_for_status()
                upstream_data = resp.json()
                # --- Transform upstream response to OpenAI format ---
                upstream_categories = upstream_data.get("categories", {})
                openai_categories = {
                    "hate": upstream_categories.get("hate", False), "hate/threatening": False,
                    "harassment": False, "harassment/threatening": False,
                    "self-harm": upstream_categories.get("self-harm", False), "self-harm/intent": False, "self-harm/instructions": False,
                    "sexual": upstream_categories.get("sexual", False), "sexual/minors": False,
                    "violence": upstream_categories.get("violence", False), "violence/graphic": False,
                }
                category_scores = {k: 1.0 if v else 0.0 for k, v in openai_categories.items()}
                flagged = upstream_data.get("overall_sentiment") == "flagged"
                result_item = {
                    "flagged": flagged,
                    "categories": openai_categories,
                    "category_scores": category_scores,
                }

                # --- NEW: Conditionally add the 'reason' field ---
                # This is a custom extension to the OpenAI spec to provide more detail.
                reason = upstream_data.get("reason")
                if reason:
                    result_item["reason"] = reason

                results.append(result_item)
    except httpx.HTTPStatusError as e:
        return JSONResponse(
            status_code=502, # Bad Gateway
            content={"error": {"message": f"Moderation failed. Upstream error: {e.response.status_code}", "type": "upstream_error", "details": e.response.text}}
        )
    except Exception as e:
        return JSONResponse(status_code=500, content={"error": {"message": "An internal error occurred during moderation.", "type": "internal_error", "details": str(e)}})
    # Build the final OpenAI-compatible response
    final_response = {
        "id": generate_random_id("modr-"),
        "model": request.model,
        "results": results,
    }
    return JSONResponse(content=final_response)

# --- Main Execution ---
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)