main.py

import os
import time
import random
import asyncio
import json
from fastapi import FastAPI, HTTPException, Depends, File, UploadFile, Form, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.security.api_key import APIKeyHeader
from pydantic import BaseModel
from typing import List, Optional
from dotenv import load_dotenv
from starlette.responses import StreamingResponse
from openai import OpenAI
from typing import List, Optional, Dict, Any
import io
import copy
from pathlib import Path
from pydub import AudioSegment
import base64, uuid, mimetypes
import struct
from google import genai
from google.genai import types
import re 

load_dotenv()

BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
EXPECTED_API_KEY = os.getenv("API_HUGGINGFACE")
API_KEY_NAME = "Authorization"
API_KEYS = [
    os.getenv("API_GEMINI_1"),
    os.getenv("API_GEMINI_2"),
    os.getenv("API_GEMINI_3"), 
    os.getenv("API_GEMINI_4"), 
    os.getenv("API_GEMINI_5"), 
]
GROQ_BASE_URL  = "https://api.groq.com/openai/v1"
WHISPER_MODEL  = "whisper-large-v3-turbo"     
SEGMENT_MINUTES = 50
GROQ_API_KEYS  = [
    os.getenv("API_GROQ_1"),
    #os.getenv("API_GROQ_2"),
    #os.getenv("API_GROQ_3"),
    #os.getenv("API_GROQ_4"),
    #os.getenv("API_GROQ_5")
]
                           
# Classi Pydantic di VALIDAZIONE Body
class ChatCompletionRequest(BaseModel):
    model: str = "gemini-2.0-flash" 
    messages: Optional[Any]
    temperature: Optional[float] = 0.8
    stream: Optional[bool] = False
    stream_options: Optional[Dict[str, Any]] = None
    class Config:
        extra = "allow"

# Server FAST API 
app = FastAPI(title="OpenAI-SDK-compatible API", version="1.0.0", description="Un wrapper FastAPI compatibile con le specifiche dell'API OpenAI.")
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Api Key GEMINI (Random della lista in modo da averne di più)
def get_gemini_apikey():
    return random.choice(API_KEYS)

# Client OpenAI
def get_openai_client():
    ''' Client OpenAI passando in modo RANDOM le Chiavi API. In questo modo posso aggirare i limiti "Quota Exceeded" '''
    api_key = get_gemini_apikey()
    return OpenAI(api_key=api_key, base_url=BASE_URL)

# Validazione API       
api_key_header = APIKeyHeader(name=API_KEY_NAME, auto_error=False)
def verify_api_key(api_key: str = Depends(api_key_header)):
    ''' Validazione Chiave API - Per ora in ENV, Token HF '''
    if not api_key:
        raise HTTPException(status_code=403, detail="API key mancante")
    if api_key != f"Bearer {EXPECTED_API_KEY}":
        raise HTTPException(status_code=403, detail="API key non valida")
    return api_key

# Correzione payload con content=None
def sanitize_messages(messages):
    """Convert None content to empty string to avoid Gemini API errors"""
    if not messages:
        return messages
    for message in messages:
        if message.get('content') is None:
            message['content'] = " "
    return messages

# Funzione per conversione Payload OpenAI to GEMINI (anomalia per ACTION) AnyOf, e property: {}
def convert_openai_schema_for_gemini(tools_schema):
    if isinstance(tools_schema, str):
        try:
            tools_schema = json.loads(tools_schema)
        except json.JSONDecodeError:
            raise ValueError("Stringa JSON non valida fornita")
    converted_schema = []
    for tool in tools_schema:
        if tool.get("type") != "function":
            converted_schema.append(tool)
            continue
        converted_tool = {"type": "function", "function": {}}
        func_def = tool.get("function", {})
        if not func_def:
            continue
        converted_tool["function"]["name"] = func_def.get("name", "")
        converted_tool["function"]["description"] = func_def.get("description", "")
        if "parameters" in func_def:
            params = func_def["parameters"]
            converted_params = {"type": "object"}
            if "properties" in params:
                converted_properties = {}
                for prop_name, prop_value in params["properties"].items():
                    cleaned = clean_schema_property(prop_value)
                    if cleaned:  
                        converted_properties[prop_name] = cleaned
                if converted_properties:
                    converted_params["properties"] = converted_properties
                else:
                    converted_params["properties"] = {"parameter": {"type": "string"}}
            else:
                converted_params["properties"] = {"parameter": {"type": "string"}}
            if "required" in params:
                converted_params["required"] = params["required"]
            converted_tool["function"]["parameters"] = converted_params
        converted_schema.append(converted_tool)
    return converted_schema

def clean_schema_property(prop):
    if not isinstance(prop, dict):
        return prop
    result = {}
    for key, value in prop.items():
        if key in ("title", "default"):
            continue
        elif key == "anyOf":
            if isinstance(value, list):
                for item in value:
                    if isinstance(item, dict) and item.get("type") != "null":
                        cleaned_item = clean_schema_property(item)
                        for k, v in cleaned_item.items():
                            if k not in result:
                                result[k] = v
                        break
        elif key == "oneOf":
            if isinstance(value, list) and len(value) > 0:
                cleaned_item = clean_schema_property(value[0])
                for k, v in cleaned_item.items():
                    if k not in result:
                        result[k] = v
            elif isinstance(value, dict):
                cleaned_item = clean_schema_property(value)
                for k, v in cleaned_item.items():
                    if k not in result:
                        result[k] = v
        elif key == "properties" and isinstance(value, dict):
            new_props = {}
            for prop_name, prop_value in value.items():
                cleaned_prop = clean_schema_property(prop_value)
                if cleaned_prop:
                    new_props[prop_name] = cleaned_prop
            if not new_props:
                new_props = {"parameter": {"type": "string"}}
            result[key] = new_props
        elif key == "items" and isinstance(value, dict):
            result[key] = clean_schema_property(value)
        elif isinstance(value, list):
            result[key] = [clean_schema_property(item) if isinstance(item, dict) else item for item in value]
        else:
            result[key] = value
    if result.get("type") == "object" and ("properties" not in result or not result["properties"]):
        result["properties"] = {"parameter": {"type": "string"}}
    return result

def convert_payload_for_gemini(payload: ChatCompletionRequest):
    if hasattr(payload, "model_dump"):
        payload_converted = json.loads(payload.model_dump_json())
    elif isinstance(payload, dict):
        payload_converted = payload.copy()
    else:
        raise ValueError("Formato payload non supportato")
    payload_converted.pop("metadata", None)
    payload_converted.pop("store", None)
    if "tools" in payload_converted:
        payload_converted["tools"] = convert_openai_schema_for_gemini(payload_converted["tools"])
    new_payload = ChatCompletionRequest.model_validate(payload_converted)
    return new_payload

# ---------------------------------- Funzioni per Chat Completion ---------------------------------------
# Chiama API (senza Streaming)
def call_api_sync(params: ChatCompletionRequest):
    ''' Chiamata API senza streaming. Se da errore 429 lo rifa'''
    try:
        client = get_openai_client()
        if params.messages:
            params.messages = sanitize_messages(params.messages)       
        params = convert_payload_for_gemini(params)
        print('------------------------------------------------------- INPUT ---------------------------------------------------------------')
        print(params)
        response_format = getattr(params, 'response_format', None)
        if response_format and getattr(response_format, 'type', None) == 'json_schema':    
            response = client.beta.chat.completions.parse(**params.model_dump())
        else:
            response = client.chat.completions.create(**params.model_dump())
        print('------------------------------------------------------- OUTPUT ---------------------------------------------------------------')
        print(response)
        print("")
        return response
    except Exception as e:
        if "429" in str(e):
            time.sleep(2)
            return call_api_sync(params)
        else:
            raise e

# Chiama API (con Streaming)
async def _resp_async_generator(params: ChatCompletionRequest):
    ''' Chiamata API con streaming. Se da errore 429 lo rifa'''
    client = get_openai_client()
    try:
        response = client.chat.completions.create(**params.model_dump())
        if params.messages:
            params.messages = sanitize_messages(params.messages)     
        params = convert_payload_for_gemini(params)
        print('------------------------------------------------------- INPUT ---------------------------------------------------------------')
        print(params.model_dump_json(indent=4))
        final_response_content = ''
        for chunk in response:
            chunk_data = chunk.to_dict() if hasattr(chunk, "to_dict") else chunk
            chunk_content = None
            if chunk.choices and chunk.choices[0].delta:
                chunk_content = chunk.choices[0].delta.content
            if chunk_content:
                final_response_content += chunk_content
            yield f"data: {json.dumps(chunk_data)}\n\n"
            await asyncio.sleep(0.01)
        yield "data: [DONE]\n\n"
        print('------------------------------------------------------- OUTPUT ---------------------------------------------------------------')
        print(final_response_content)
    except Exception as e:
        if "429" in str(e):
            await asyncio.sleep(2)
            async for item in _resp_async_generator(params):
                yield item
        else:
            error_data = {"error": str(e)}
            yield f"data: {json.dumps(error_data)}\n\n"


def get_openai_client():
    ''' Client OpenAI passando in modo RANDOM le Chiavi API. In questo modo posso aggirare i limiti "Quota Exceeded" '''
    api_key = random.choice(API_KEYS)
    return OpenAI(api_key=api_key, base_url=BASE_URL)

# API Whisper Audio:
FORMAT_ALIASES = {
    "mpeg": "mp3",
    "x-wav": "wav",
    "vnd.wave": "wav",
    "x-m4a": "m4a",
    "x-aac": "aac",
}

def _detect_format(upload_file: UploadFile) -> str:
    """Rileva il formato audio dal MIME-type o dall'estensione, con alias safe."""
    if upload_file.content_type and upload_file.content_type.startswith("audio/"):
        fmt = upload_file.content_type.split("/", 1)[1]
    else:
        fmt = Path(upload_file.filename).suffix.lstrip(".").lower()
    return FORMAT_ALIASES.get(fmt, fmt)

def _split_audio_to_mp3_chunks(audio_bytes: bytes, input_format: str, minutes: int):
    """ Converte (se serve) e splitta. Lascia che ffmpeg auto-rilevi il formato passando format=None: è più sicuro e ignora alias sbagliati. """
    try:
        audio = AudioSegment.from_file(io.BytesIO(audio_bytes)) 
    except Exception:
        audio = AudioSegment.from_file(io.BytesIO(audio_bytes), format=input_format)
    chunk_len_ms = minutes * 60 * 1000
    for start_ms in range(0, len(audio), chunk_len_ms):
        chunk = audio[start_ms : start_ms + chunk_len_ms]
        buf = io.BytesIO()
        chunk.export(buf, format="mp3")
        yield buf.getvalue()

def _transcribe_chunk(chunk_bytes: bytes,
                      model: str,
                      language: str,
                      response_format: str = "json") -> str:
    bio = io.BytesIO(chunk_bytes)
    bio.name = "chunk.mp3"
    resp = call_whisper_api(
        bio,
        model=model,
        language=language,
        response_format=response_format
    )
    if isinstance(resp, str):
        return resp
    if hasattr(resp, "text"):
        return resp.text
    return resp.get("text", "")

def get_whisper_client():
    api_key = random.choice(GROQ_API_KEYS)
    return OpenAI(api_key=api_key, base_url=GROQ_BASE_URL)

def call_whisper_api(audio_file: io.BytesIO,
                     model: str = WHISPER_MODEL,
                     language: str = "it",
                     response_format: str = "json"):
    try:
        client = get_whisper_client()
        return client.audio.transcriptions.create(
            file=audio_file,
            model=model,
            language=language,
            response_format=response_format
        )
    except Exception as e:
        if "429" in str(e):
            time.sleep(2)
            return call_whisper_api(audio_file, model, language, response_format)
        raise e

class SpeechRequest(BaseModel):
    model: Optional[str] = "gemini-2.5-flash-preview-tts" 
    input: str
    voice: Optional[str] = "Kore"
    speed: Optional[float] = 1.0           
    response_format: Optional[str] = "wav"
    class Config:
        extra = "allow"    

class SpeechResponse(BaseModel):
    model: str
    response_format: str
    voice: str
    audio: str   

def convert_format(audio_bytes: bytes, from_fmt: str, to_fmt: str) -> bytes:
    """
    Converte i byte audio da 'from_fmt' a 'to_fmt' usando pydub/ffmpeg.
    Supporta mp3, wav, opus, flac, aac, pcm (raw little-endian 16-bit).
    """
    if from_fmt == to_fmt:
        return audio_bytes

    audio = AudioSegment.from_file(io.BytesIO(audio_bytes), format=from_fmt)
    buf = io.BytesIO()
    if to_fmt == "pcm":                     # raw PCM 16-bit LE
        audio.export(buf, format="raw")
    else:
        audio.export(buf, format=to_fmt)
    return buf.getvalue()

def parse_audio_mime_type(mime_type: str) -> dict[str, int | None]:
    """Parses bits per sample and rate from an audio MIME type string """
    bits_per_sample = 16
    rate = 24000
    parts = mime_type.split(";")
    for param in parts: 
        param = param.strip()
        if param.lower().startswith("rate="):
            try:
                rate_str = param.split("=", 1)[1]
                rate = int(rate_str)
            except (ValueError, IndexError):
                pass # Keep rate as default
        elif param.startswith("audio/L"):
            try:
                bits_per_sample = int(param.split("L", 1)[1])
            except (ValueError, IndexError):
                pass # Keep bits_per_sample as default if conversion fails
    return {"bits_per_sample": bits_per_sample, "rate": rate}

def convert_to_wav(audio_data: bytes, mime_type: str) -> bytes:
    """Generates a WAV file header for the given audio data and parameters."""
    parameters = parse_audio_mime_type(mime_type)
    bits_per_sample = parameters["bits_per_sample"]
    sample_rate = parameters["rate"]
    num_channels = 1
    data_size = len(audio_data)
    bytes_per_sample = bits_per_sample // 8
    block_align = num_channels * bytes_per_sample
    byte_rate = sample_rate * block_align
    chunk_size = 36 + data_size  
    header = struct.pack(
        "<4sI4s4sIHHIIHH4sI",
        b"RIFF",          # ChunkID
        chunk_size,       # ChunkSize (total file size - 8 bytes)
        b"WAVE",          # Format
        b"fmt ",          # Subchunk1ID
        16,               # Subchunk1Size (16 for PCM)
        1,                # AudioFormat (1 for PCM)
        num_channels,     # NumChannels
        sample_rate,      # SampleRate
        byte_rate,        # ByteRate
        block_align,      # BlockAlign
        bits_per_sample,  # BitsPerSample
        b"data",          # Subchunk2ID
        data_size         # Subchunk2Size (size of audio data)
    )
    return header + audio_data

# Generazione Audio
def generate_audio(model: str, 
                   content: str,
                   speaker1: str = "Kore",
                   speaker2: str = "Schedar") -> bytes:
    """Restituisce i byte WAV generati da Gemini-TTS (multi-speaker)."""
    client = genai.Client(api_key=get_gemini_apikey())
    contents = [types.Content(role="user", parts=[types.Part.from_text(text=content)])]
    cfg = types.GenerateContentConfig(
        temperature=1,
        response_modalities=["audio"],
        speech_config=types.SpeechConfig(
            multi_speaker_voice_config=types.MultiSpeakerVoiceConfig(
                speaker_voice_configs=[
                    types.SpeakerVoiceConfig(
                        speaker="Speaker 1",
                        voice_config=types.VoiceConfig(
                            prebuilt_voice_config=types.PrebuiltVoiceConfig(
                                voice_name=speaker1
                            )
                        ),
                    ),
                    types.SpeakerVoiceConfig(
                        speaker="Speaker 2",
                        voice_config=types.VoiceConfig(
                            prebuilt_voice_config=types.PrebuiltVoiceConfig(
                                voice_name=speaker2
                            )
                        ),
                    ),
                ]
            ),
        ),
    )

    for chunk in client.models.generate_content_stream(
        model=model, contents=contents, config=cfg
    ):
        part = chunk.candidates[0].content.parts[0]
        if part.inline_data and part.inline_data.data:
            data = part.inline_data.data
            if mimetypes.guess_extension(part.inline_data.mime_type) is None:
                data = convert_to_wav(data, part.inline_data.mime_type)
            return data
    raise RuntimeError("Nessun dato audio ricevuto")


# ---------------------------------- Metodi API ---------------------------------------
@app.get("/")
def read_general(): 
    return {"response": "Benvenuto"}

@app.get("/health")
async def health_check():
    return {"message": "success"}

@app.post("/v1/chat/completions", dependencies=[Depends(verify_api_key)])
async def chat_completions(req: ChatCompletionRequest):
    try:    
        if not req.messages:
            raise HTTPException(status_code=400, detail="Nessun messaggio fornito")
        if not req.stream:
            return call_api_sync(req)
        else:
            return StreamingResponse(_resp_async_generator(req), media_type="application/x-ndjson")            
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/v1/audio/transcriptions", dependencies=[Depends(verify_api_key)])
async def audio_transcriptions_endpoint(
    file: UploadFile = File(...),
    model: str = Form(WHISPER_MODEL),
    language: str = Form("it"),
    response_format: str = Form("text"),
    segment_minutes: int = Form(SEGMENT_MINUTES)):
    try:
        raw_bytes = await file.read()
        input_fmt  = _detect_format(file) or "mp3"
        chunks = list(_split_audio_to_mp3_chunks(raw_bytes, input_fmt, segment_minutes))
        if not chunks:
            raise ValueError("Audio vuoto o formato non riconosciuto")
        transcripts = [_transcribe_chunk(c, model, language, response_format) for c in chunks]
        final_text = "\n\n".join(transcripts)
        return {
            "model": model,
            "language": language,
            "segments": len(transcripts),
            "segment_minutes": segment_minutes,
            "text": final_text,
        }
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/v1/audio/speech", dependencies=[Depends(verify_api_key)],
          response_model=SpeechResponse)
async def audio_speech_endpoint(req: SpeechRequest,  request: Request):
    try:
        voices = re.split(r"[;,|]", req.voice)
        speaker1 = voices[0].strip()
        speaker2 = voices[1].strip() if len(voices) > 1 else "Schedar"
        print('------------------------------------------------------- INPUT ---------------------------------------------------------------')
        print(req.voice)
        print(req.input)
        wav_bytes = generate_audio(
            model=req.model,
            content=req.input,
            speaker1=speaker1,
            speaker2=speaker2
        )
        audio_bytes = convert_format(wav_bytes, "wav", req.response_format)
        audio_fmt   = req.response_format.lower()
        audio_bytes = convert_format(wav_bytes, "wav", audio_fmt)
        return StreamingResponse(
            io.BytesIO(audio_bytes),
            media_type="application/octet-stream",
            headers={
                "Content-Disposition": f'attachment; filename="audio.{audio_fmt}"',
                "X-OpenAI-Response-Format": audio_fmt,
            },
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

if __name__ == "__main__":
    import uvicorn
    uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)