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)