main.py

import os
from dotenv import load_dotenv
from fastapi import FastAPI, HTTPException, Request
from fastapi.responses import StreamingResponse, HTMLResponse, JSONResponse, FileResponse  
from pydantic import BaseModel
import httpx
from pathlib import Path  # Import Path from pathlib
import requests
import re
import cloudscraper
import json
from typing import Optional
import datetime
from usage_tracker import UsageTracker
usage_tracker = UsageTracker()
load_dotenv() #idk why this shi

app = FastAPI()

# Get API keys and secret endpoint from environment variables
api_keys_str = os.getenv('API_KEYS') #deprecated -_-
valid_api_keys = api_keys_str.split(',') if api_keys_str else []
secret_api_endpoint = os.getenv('SECRET_API_ENDPOINT')
secret_api_endpoint_2 = os.getenv('SECRET_API_ENDPOINT_2')
secret_api_endpoint_3 = os.getenv('SECRET_API_ENDPOINT_3')  # New endpoint for searchgpt
image_endpoint = os.getenv("IMAGE_ENDPOINT")
ENDPOINT_ORIGIN = os.getenv('ENDPOINT_ORIGIN') 

# Validate if the main secret API endpoints are set
if not secret_api_endpoint or not secret_api_endpoint_2 or not secret_api_endpoint_3:
    raise HTTPException(status_code=500, detail="API endpoint(s) are not configured in environment variables.")

# Define models that should use the secondary endpoint
alternate_models = {"gpt-4o-mini", "claude-3-haiku", "llama-3.1-70b", "mixtral-8x7b"}

available_model_ids = []
class Payload(BaseModel):
    model: str
    messages: list
    stream: bool
@app.get("/favicon.ico")
async def favicon():
    # The favicon.ico file is in the same directory as the app
    favicon_path = Path(__file__).parent / "favicon.ico"
    return FileResponse(favicon_path, media_type="image/x-icon")
    
def generate_search(query: str, systemprompt: Optional[str] = None, stream: bool = True) -> str:
    headers = {"User-Agent": ""}
    
    # Use the provided system prompt, or default to "Be Helpful and Friendly"
    system_message = systemprompt or "Be Helpful and Friendly"
    
    # Create the prompt history with the user query and system message
    prompt = [
        {"role": "user", "content": query},
    ]
    
    prompt.insert(0, {"content": system_message, "role": "system"})
    
    # Prepare the payload for the API request
    payload = {
        "is_vscode_extension": True,
        "message_history": prompt,
        "requested_model": "searchgpt",
        "user_input": prompt[-1]["content"],
    }
    
    # Send the request to the chat endpoint
    response = requests.post(secret_api_endpoint_3, headers=headers, json=payload, stream=True)
    
    streaming_text = ""
    
    # Process the streaming response
    for value in response.iter_lines(decode_unicode=True):
        if value.startswith("data: "):  
            try:
                json_modified_value = json.loads(value[6:])
                content = json_modified_value.get("choices", [{}])[0].get("delta", {}).get("content", "")

                if content.strip():  # Only process non-empty content
                    cleaned_response = {
                        "created": json_modified_value.get("created"),
                        "id": json_modified_value.get("id"),
                        "model": "searchgpt",
                        "object": "chat.completion",
                        "choices": [
                            {
                                "message": {
                                    "content": content
                                }
                            }
                        ]
                    }
                    
                    if stream:
                        yield f"data: {json.dumps(cleaned_response)}\n\n"
                    
                    streaming_text += content
            except json.JSONDecodeError:
                continue
    
    if not stream:
        yield streaming_text

@app.get("/ping")
async def ping():
    start_time = datetime.datetime.now()
    response_time = (datetime.datetime.now() - start_time).total_seconds()
    return {"message": "pong", "response_time": f"{response_time:.6f} seconds"}
    
@app.get("/searchgpt")
async def search_gpt(q: str, stream: Optional[bool] = False, systemprompt: Optional[str] = None):
    if not q:
        raise HTTPException(status_code=400, detail="Query parameter 'q' is required")
    usage_tracker.record_request(endpoint="/searchgpt")
    if stream:
        return StreamingResponse(
            generate_search(q, systemprompt=systemprompt, stream=True),
            media_type="text/event-stream"
        )
    else:
        # For non-streaming, collect the text and return as JSON response
        response_text = "".join([chunk for chunk in generate_search(q, systemprompt=systemprompt, stream=False)])
        return JSONResponse(content={"response": response_text})
@app.get("/", response_class=HTMLResponse)
async def root():
    # Open and read the content of index.html (in the same folder as the app)
    file_path = "index.html"

    try:
        with open(file_path, "r") as file:
            html_content = file.read()
        return HTMLResponse(content=html_content)
    except FileNotFoundError:
        return HTMLResponse(content="<h1>File not found</h1>", status_code=404)

async def get_models():
    try:
        # Load the models from models.json in the same folder
        file_path = Path(__file__).parent / 'models.json'
        with open(file_path, 'r') as f:
            return json.load(f)
    except FileNotFoundError:
        raise HTTPException(status_code=404, detail="models.json not found")
    except json.JSONDecodeError:
        raise HTTPException(status_code=500, detail="Error decoding models.json")

@app.get("/models")
async def fetch_models():
    return await get_models()
server_status = True
@app.post("/chat/completions")
@app.post("api/v1/chat/completions")
async def get_completion(payload: Payload, request: Request):
    # Check server status
    if not server_status:
        return JSONResponse(
            status_code=503,
            content={"message": "Server is under maintenance. Please try again later."}
        )
    
    model_to_use = payload.model if payload.model else "gpt-4o-mini"
        
    # Validate model availability
    if model_to_use not in available_model_ids:
        raise HTTPException(
            status_code=400,
            detail=f"Model '{model_to_use}' is not available. Check /models for the available model list."
        )
        
    usage_tracker.record_request(model=model_to_use, endpoint="/chat/completions")
        
    # Prepare payload
    payload_dict = payload.dict()
    payload_dict["model"] = model_to_use
        
    # Select the appropriate endpoint
    endpoint = secret_api_endpoint_2 if model_to_use in alternate_models else secret_api_endpoint
    
    # Current time and IP logging
    current_time = (datetime.datetime.utcnow() + datetime.timedelta(hours=5, minutes=30)).strftime("%Y-%m-%d %I:%M:%S %p")
    aaip = request.client.host
    print(f"Time: {current_time}, {aaip}")
    # print(payload_dict) 
    scraper = cloudscraper.create_scraper()    
    async def stream_generator(payload_dict):
        
        # Prepare custom headers
        custom_headers = {
            'DNT': '1',
            # 'Origin': ENDPOINT_ORIGIN,
            'Priority': 'u=1, i',
            # 'Referer': ENDPOINT_ORIGIN
        }
        
        try:
            # Send POST request using CloudScraper with custom headers
            response = scraper.post(
                f"{endpoint}/v1/chat/completions", 
                json=payload_dict, 
                headers=custom_headers,
                stream=True
            )
            
            # Error handling remains the same as in previous version
            if response.status_code == 422:
                raise HTTPException(status_code=422, detail="Unprocessable entity. Check your payload.")
            elif response.status_code == 400:
                raise HTTPException(status_code=400, detail="Bad request. Verify input data.")
            elif response.status_code == 403:
                raise HTTPException(status_code=403, detail="Forbidden. You do not have access to this resource.")
            elif response.status_code == 404:
                raise HTTPException(status_code=404, detail="The requested resource was not found.")
            elif response.status_code >= 500:
                raise HTTPException(status_code=500, detail="Server error. Try again later.")
            
            # Stream response lines to the client
            for line in response.iter_lines():
                if line:
                    yield line.decode('utf-8') + "\n"
        except requests.exceptions.RequestException as req_err:
            # Handle request-specific errors
            print(response.text)
            raise HTTPException(status_code=500, detail=f"Request failed: {req_err}")
        except Exception as e:
            # Handle unexpected errors
            print(response.text)
            raise HTTPException(status_code=500, detail=f"An unexpected error occurred: {e}")
    
    return StreamingResponse(stream_generator(payload_dict), media_type="application/json")
# Remove the duplicated endpoint and combine the functionality
@app.api_route("/images/generations", methods=["GET", "POST"])  # Support both GET and POST
async def generate_image(
    prompt: Optional[str] = None, 
    model: str = "flux",  # Default model
    seed: Optional[int] = None,
    width: Optional[int] = None,
    height: Optional[int] = None,
    nologo: Optional[bool] = True,
    private: Optional[bool] = None,
    enhance: Optional[bool] = None,
    request: Request = None,  # Access raw POST data
):
    """
    Generate an image using the Image Generation API.
    """
    # Validate the image endpoint
    if not image_endpoint:
        raise HTTPException(status_code=500, detail="Image endpoint not configured in environment variables.")
    usage_tracker.record_request(endpoint="/images/generations")
    # Handle GET and POST prompts
    if request.method == "POST":
        try:
            body = await request.json()  # Parse JSON body
            prompt = body.get("prompt", "").strip()
            if not prompt:
                raise HTTPException(status_code=400, detail="Prompt cannot be empty")
        except Exception:
            raise HTTPException(status_code=400, detail="Invalid JSON payload")
    elif request.method == "GET":
        if not prompt or not prompt.strip():
            raise HTTPException(status_code=400, detail="Prompt cannot be empty")
        prompt = prompt.strip()

    # Sanitize and encode the prompt
    encoded_prompt = httpx.QueryParams({'prompt': prompt}).get('prompt')

    # Construct the URL with the encoded prompt
    base_url = image_endpoint.rstrip('/')  # Remove trailing slash if present
    url = f"{base_url}/{encoded_prompt}"

    # Prepare query parameters with validation
    params = {}

    if model and isinstance(model, str):
        params['model'] = model
    if seed is not None and isinstance(seed, int):
        params['seed'] = seed
    if width is not None and isinstance(width, int) and 64 <= width <= 2048:
        params['width'] = width
    if height is not None and isinstance(height, int) and 64 <= height <= 2048:
        params['height'] = height
    if nologo is not None:
        params['nologo'] = str(nologo).lower()
    if private is not None:
        params['private'] = str(private).lower()
    if enhance is not None:
        params['enhance'] = str(enhance).lower()

    try:
        timeout = httpx.Timeout(60.0)  # Set a reasonable timeout
        async with httpx.AsyncClient(timeout=timeout) as client:
            response = await client.get(url, params=params, follow_redirects=True)

            # Check for various error conditions
            if response.status_code == 404:
                raise HTTPException(status_code=404, detail="Image generation service not found")
            elif response.status_code == 400:
                raise HTTPException(status_code=400, detail="Invalid parameters provided to image service")
            elif response.status_code == 429:
                raise HTTPException(status_code=429, detail="Too many requests to image service")
            elif response.status_code != 200:
                raise HTTPException(
                    status_code=response.status_code,
                    detail=f"Image generation failed with status code {response.status_code}"
                )

            # Verify content type
            content_type = response.headers.get('content-type', '')
            if not content_type.startswith('image/'):
                raise HTTPException(
                    status_code=500,
                    detail=f"Unexpected content type received: {content_type}"
                )

            return StreamingResponse(
                response.iter_bytes(),
                media_type=content_type,
                headers={
                    'Cache-Control': 'no-cache',
                    'Pragma': 'no-cache'
                }
            )

    except httpx.TimeoutException:
        raise HTTPException(status_code=504, detail="Image generation request timed out")
    except httpx.RequestError as e:
        raise HTTPException(status_code=500, detail=f"Failed to contact image service: {str(e)}")
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Unexpected error during image generation: {str(e)}")
@app.get("/playground", response_class=HTMLResponse)
async def playground():
    # Open and read the content of playground.html (in the same folder as the app)
    file_path = "playground.html"

    try:
        with open(file_path, "r") as file:
            html_content = file.read()
        return HTMLResponse(content=html_content)
    except FileNotFoundError:
        return HTMLResponse(content="<h1>playground.html not found</h1>", status_code=404)
        
def load_model_ids(json_file_path):
    try:
        with open(json_file_path, 'r') as f:
            models_data = json.load(f)
            # Extract 'id' from each model object
            model_ids = [model['id'] for model in models_data if 'id' in model]
            return model_ids
    except FileNotFoundError:
        print("Error: models.json file not found.")
        return []
    except json.JSONDecodeError:
        print("Error: Invalid JSON format in models.json.")
        return []
@app.get("/usage")
async def get_usage(days: int = 7):
    """Retrieve usage statistics"""
    return usage_tracker.get_usage_summary(days)

@app.get("/usage/page", response_class=HTMLResponse)
async def usage_page():
    """Serve an HTML page showing usage statistics"""
    # Retrieve usage data
    usage_data = usage_tracker.get_usage_summary()
    # Model Usage Table Rows
    model_usage_rows = "\n".join([
        f"""
        <tr>
            <td>{model}</td>
            <td>{model_data['total_requests']}</td>
            <td>{model_data['first_used']}</td>
            <td>{model_data['last_used']}</td>
        </tr>
        """ for model, model_data in usage_data['models'].items()
    ])
    # API Endpoint Usage Table Rows
    api_usage_rows = "\n".join([
        f"""
        <tr>
            <td>{endpoint}</td>
            <td>{endpoint_data['total_requests']}</td>
            <td>{endpoint_data['first_used']}</td>
            <td>{endpoint_data['last_used']}</td>
        </tr>
        """ for endpoint, endpoint_data in usage_data['api_endpoints'].items()
    ])
    # Daily Usage Table Rows
    daily_usage_rows = "\n".join([
        "\n".join([
            f"""
            <tr>
                <td>{date}</td>
                <td>{entity}</td>
                <td>{requests}</td>
            </tr>
            """ for entity, requests in date_data.items()
        ]) for date, date_data in usage_data['recent_daily_usage'].items()
    ])
    
    html_content = f"""
    <!DOCTYPE html>
    <html lang="en">
    <head>
        <meta charset="UTF-8">
        <title>Lokiai AI - Usage Statistics</title>
        <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600&display=swap" rel="stylesheet">
        <style>
            :root {{
                --bg-dark: #0f1011;
                --bg-darker: #070708;
                --text-primary: #e6e6e6;
                --text-secondary: #8c8c8c;
                --border-color: #2c2c2c;
                --accent-color: #3a6ee0;
                --accent-hover: #4a7ef0;
            }}
            body {{
                font-family: 'Inter', sans-serif;
                background-color: var(--bg-dark);
                color: var(--text-primary);
                max-width: 1200px;
                margin: 0 auto;
                padding: 40px 20px;
                line-height: 1.6;
            }}
            .logo {{
                display: flex;
                align-items: center;
                justify-content: center;
                margin-bottom: 30px;
            }}
            .logo h1 {{
                font-weight: 600;
                font-size: 2.5em;
                color: var(--text-primary);
                margin-left: 15px;
            }}
            .logo img {{
                width: 60px;
                height: 60px;
                border-radius: 10px;
            }}
            .container {{
                background-color: var(--bg-darker);
                border-radius: 12px;
                padding: 30px;
                box-shadow: 0 15px 40px rgba(0,0,0,0.3);
                border: 1px solid var(--border-color);
            }}
            h2, h3 {{
                color: var(--text-primary);
                border-bottom: 2px solid var(--border-color);
                padding-bottom: 10px;
                font-weight: 500;
            }}
            .total-requests {{
                background-color: var(--accent-color);
                color: white;
                text-align: center;
                padding: 15px;
                border-radius: 8px;
                margin-bottom: 30px;
                font-weight: 600;
                letter-spacing: -0.5px;
            }}
            table {{
                width: 100%;
                border-collapse: separate;
                border-spacing: 0;
                margin-bottom: 30px;
                background-color: var(--bg-dark);
                border-radius: 8px;
                overflow: hidden;
            }}
            th, td {{
                border: 1px solid var(--border-color);
                padding: 12px;
                text-align: left;
                transition: background-color 0.3s ease;
            }}
            th {{
                background-color: #1e1e1e;
                color: var(--text-primary);
                font-weight: 600;
                text-transform: uppercase;
                font-size: 0.9em;
            }}
            tr:nth-child(even) {{
                background-color: rgba(255,255,255,0.05);
            }}
            tr:hover {{
                background-color: rgba(62,100,255,0.1);
            }}
            @media (max-width: 768px) {{
                .container {{
                    padding: 15px;
                }}
                table {{
                    font-size: 0.9em;
                }}
            }}
        </style>
    </head>
    <body>
        <div class="container">
            <div class="logo">
                <img src="data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMjAwIiBoZWlnaHQ9IjIwMCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj48cGF0aCBkPSJNMTAwIDM1TDUwIDkwaDEwMHoiIGZpbGw9IiMzYTZlZTAiLz48Y2lyY2xlIGN4PSIxMDAiIGN5PSIxNDAiIHI9IjMwIiBmaWxsPSIjM2E2ZWUwIi8+PC9zdmc+" alt="Lokai AI Logo">
                <h1>Lokiai AI</h1>
            </div>
            
            <div class="total-requests">
                Total API Requests: {usage_data['total_requests']}
            </div>
            
            <h2>Model Usage</h2>
            <table>
                <tr>
                    <th>Model</th>
                    <th>Total Requests</th>
                    <th>First Used</th>
                    <th>Last Used</th>
                </tr>
                {model_usage_rows}
            </table>
            
            <h2>API Endpoint Usage</h2>
            <table>
                <tr>
                    <th>Endpoint</th>
                    <th>Total Requests</th>
                    <th>First Used</th>
                    <th>Last Used</th>
                </tr>
                {api_usage_rows}
            </table>
            
            <h2>Daily Usage (Last 7 Days)</h2>
            <table>
                <tr>
                    <th>Date</th>
                    <th>Entity</th>
                    <th>Requests</th>
                </tr>
                {daily_usage_rows}
            </table>
        </div>
    </body>
    </html>
    """
    return HTMLResponse(content=html_content)
@app.get("/meme")
async def get_meme():
    try:
        response = requests.get("https://meme-api.com/gimme")
        response_data = response.json()

        meme_url = response_data.get("url")

        if meme_url:
            def stream_image():
                with requests.get(meme_url, stream=True) as image_response:
                    for chunk in image_response.iter_content(chunk_size=1024):
                        yield chunk

            return StreamingResponse(stream_image(), media_type="image/png")
        else:
            raise HTTPException(status_code=404, detail="No mimi found :(")

    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))
        
@app.on_event("startup")
async def startup_event():
    global available_model_ids
    available_model_ids = load_model_ids("models.json")
    print(f"Loaded model IDs: {available_model_ids}")
    print("API endpoints:")
    print("GET /")
    print("GET /models")
    print("GET /searchgpt")
    print("POST /chat/completions")
    print("GET /images/generations") 
    
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)