import os import time from pydantic import BaseModel from fastapi import FastAPI, HTTPException, Query, Request from fastapi.responses import FileResponse from fastapi.middleware.cors import CORSMiddleware from langchain.chains import LLMChain from langchain.prompts import PromptTemplate from TextGen.suno import custom_generate_audio, get_audio_information from langchain_google_genai import ( ChatGoogleGenerativeAI, HarmBlockThreshold, HarmCategory, ) from TextGen import app from gradio_client import Client from typing import List class Message(BaseModel): npc: str | None = None messages: List[str] | None = None class VoiceMessage(BaseModel): npc: str | None = None input: str | None = None language: str | None = "en" genre:str | None = "Male" song_base_api=os.environ["VERCEL_API"] my_hf_token=os.environ["HF_TOKEN"] tts_client = Client("https://jofthomas-xtts.hf.space/",hf_token=my_hf_token) main_npcs={ "Blacksmith":"./voices/Blacksmith.mp3", "Herbalist":"./voices/female.mp3", "Bard":"./voices/Bard_voice.mp3" } class Generate(BaseModel): text:str def generate_text(messages: List[str]): print(messages) promptmessages[-1] prompt = PromptTemplate(template=prompt, input_variables=['Prompt']) # Initialize the LLM llm = ChatGoogleGenerativeAI( model="gemini-pro", safety_settings={ HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE, }, ) llmchain = LLMChain( prompt=prompt, llm=llm ) llm_response = llmchain.run({"Prompt": prompt}) return Generate(text=llm_response) app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) @app.get("/", tags=["Home"]) def api_home(): return {'detail': 'Welcome to FastAPI TextGen Tutorial!'} @app.post("/api/generate", summary="Generate text from prompt", tags=["Generate"], response_model=Generate) def inference(message: Message): return generate_text(prompt=message.input) #Dummy function for now def determine_vocie_from_npc(npc,genre): if npc in main_npcs: return main_npcs[npc] else: if genre =="Male": "./voices/default_male.mp3" if genre=="Female": return"./voices/default_female.mp3" else: return "./voices/narator_out.wav" @app.post("/generate_wav") async def generate_wav(message:VoiceMessage): try: voice=determine_vocie_from_npc(message.npc, message.genre) # Use the Gradio client to generate the wav file result = tts_client.predict( message.input, # str in 'Text Prompt' Textbox component message.language, # str in 'Language' Dropdown component voice, # str (filepath on your computer (or URL) of file) in 'Reference Audio' Audio component voice, # str (filepath on your computer (or URL) of file) in 'Use Microphone for Reference' Audio component False, # bool in 'Use Microphone' Checkbox component False, # bool in 'Cleanup Reference Voice' Checkbox component False, # bool in 'Do not use language auto-detect' Checkbox component True, # bool in 'Agree' Checkbox component fn_index=1 ) # Get the path of the generated wav file wav_file_path = result[1] # Return the generated wav file as a response return FileResponse(wav_file_path, media_type="audio/wav", filename="output.wav") except Exception as e: raise HTTPException(status_code=500, detail=str(e)) @app.get("/generate_song") async def generate_song(text: str): try: data = custom_generate_audio({ "prompt": f"{text}", "make_instrumental": False, "wait_audio": False }) ids = f"{data[0]['id']},{data[1]['id']}" print(f"ids: {ids}") for _ in range(60): data = get_audio_information(ids) if data[0]["status"] == 'streaming': print(f"{data[0]['id']} ==> {data[0]['audio_url']}") print(f"{data[1]['id']} ==> {data[1]['audio_url']}") break # sleep 5s time.sleep(5) except: print("Error")