First commit

app.py

@@ -1,739 +1,273 @@
+# ===================================================================================
+# 1. SETUP AND IMPORTS
+# ===================================================================================
 from __future__ import annotations
-# Downloading files of the server
 import os
 import requests
-def download_file(url, save_path):
-    response = requests.get(url)
-    with open(save_path, 'wb') as file:
-        file.write(response.content)
-file_names = [
-    'cloee-1.wav',
-    'julian-bedtime-style-1.wav',
-    'julian-bedtime-style-2.wav',
-    'pirate_by_coqui.wav',
-    'thera-1.wav'
-]
-base_url = 'https://raw.githubusercontent.com/ruslanmv/ai-story-server/main/voices/'
-save_folder = 'voices/'
-if not os.path.exists(save_folder):
-    os.makedirs(save_folder)
-for file_name in file_names:
-    url = base_url + file_name
-    save_path = os.path.join(save_folder, file_name)
-    download_file(url, save_path)
-    print(f'Downloaded {file_name}')
-requirements_url = 'https://raw.githubusercontent.com/ruslanmv/ai-story-server/main/requirements.txt'
-save_path = 'requirements.txt'
-download_file(requirements_url, save_path)  
-#os.system('pip install gradio==3.48.0')
-os.system('pip install -r requirements.txt')
-os.system('pip install python-dotenv')
-os.system('pip install ipython')
-from IPython.display import clear_output
-clear_output()
-import os
-import shutil
-from IPython.display import clear_output
-# Use GPU
-def is_nvidia_smi_available():
-    return shutil.which("nvidia-smi") is not None
-if is_nvidia_smi_available():
-    gpu_info = os.popen("nvidia-smi").read()
-    if gpu_info.find('failed') >= 0:
-        print('Not connected to a GPU')
-        is_gpu = False
-    else:
-        print(gpu_info)
-        is_gpu = True
-else:
-    print('nvidia-smi command not found')
-    print('Not connected to a GPU')
-    is_gpu = False
-import os
-import dotenv
-# Load the environment variables from the .env file
-# You can change the default secret
-with open(".env", "w") as env_file:
-    env_file.write("SECRET_TOKEN=secret")
-dotenv.load_dotenv()
-# Access the value of the SECRET_TOKEN variable
-secret_token = os.getenv("SECRET_TOKEN")
-import os
-#download for mecab
-# Check if unidic is installed
-os.system("python -m unidic download")
-
-# By using XTTS you agree to CPML license https://coqui.ai/cpml
-os.environ["COQUI_TOS_AGREED"] = "1"
-# NOTE: for streaming will require gradio audio streaming fix
-# pip install --upgrade -y gradio==0.50.2 git+https://github.com/gorkemgoknar/gradio.git@patch-1
-#Now you’re ready to install 🤗 Transformers with the following command:
-if not is_gpu:
-  #For CPU-support only, Transformers and PyTorch with:
-  os.system('pip install transformers[tf-cpu]')
-  #os.system('pip install  transformers[torch] accelerate==0.26.1')
-  #pip install 'transformers[tf-cpu]'   #Transformers and TensorFlow 2.0:
-  os.system('pip install llama-cpp-python==0.2.11')
-else:
-    os.system('pip install transformers[torch]')
-    # we need to compile a CUBLAS version
-    # Or get it from  https://jllllll.github.io/llama-cpp-python-cuBLAS-wheels/
-    os.system('CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python==0.2.11')
-clear_output()
-
-import textwrap
-from scipy.io.wavfile import write
-from pydub import AudioSegment
-import gradio as gr
-import numpy as np
-import torch
-import nltk  # we'll use this to split into sentences
-nltk.download("punkt")
-import noisereduce as nr
-import subprocess
-import langid
-import uuid
-import emoji
-import pathlib
+import base64
 import datetime
-from scipy.io.wavfile import write
-from pydub import AudioSegment
+import struct
 import re
-import io, wave
-import librosa
-import torchaudio
-from TTS.api import TTS
-from TTS.tts.configs.xtts_config import XttsConfig
-from TTS.tts.models.xtts import Xtts
-from TTS.utils.generic_utils import get_user_data_dir
-import gradio as gr
-import os
+import textwrap
 import time
+import uuid
+
+# --- Hugging Face Spaces & ZeroGPU ---
+import spaces # Required for ZeroGPU
 import gradio as gr
+
+# --- Core ML & Data Libraries ---
+import torch
 import numpy as np
-from transformers import pipeline
-from gradio_client import Client
-from huggingface_hub import InferenceClient
-clear_output()
+from huggingface_hub import HfApi, hf_hub_download
+from llama_cpp import Llama
 
-# This will trigger downloading model
-print("Downloading if not downloaded Coqui XTTS V2")
+# --- TTS Libraries ---
+from TTS.tts.configs.xtts_config import XttsConfig
+from TTS.tts.models.xtts import Xtts
 from TTS.utils.manage import ModelManager
-model_name = "tts_models/multilingual/multi-dataset/xtts_v2"
-ModelManager().download_model(model_name)
-model_path = os.path.join(get_user_data_dir("tts"), model_name.replace("/", "--"))
-print("XTTS downloaded")
-if is_gpu:
-    use_deepspeed=True
-else:
-    use_deepspeed=False
-print("Loading XTTS")
-config = XttsConfig()
-config.load_json(os.path.join(model_path, "config.json"))
-model = Xtts.init_from_config(config)
-model.load_checkpoint(
-    config,
-    checkpoint_path=os.path.join(model_path, "model.pth"),
-    vocab_path=os.path.join(model_path, "vocab.json"),
-    eval=True,
-    use_deepspeed=use_deepspeed,
-)
-print("Done loading TTS")
-#####llm_model = os.environ.get("LLM_MODEL", "mistral") # or "zephyr"
-title = "Voice chat with Zephyr/Mistral and Coqui XTTS"
-DESCRIPTION = """# Voice chat with Zephyr/Mistral and Coqui XTTS"""
-css = """.toast-wrap { display: none !important } """
-from huggingface_hub import HfApi
-
-HF_TOKEN = os.environ.get("HF_TOKEN")
-# will use api to restart space on a unrecoverable error
-api = HfApi(token=HF_TOKEN)
-
-# config changes  ---------------
-import base64
-repo_id = "ruslanmv/ai-story-server"
-SECRET_TOKEN = os.getenv('SECRET_TOKEN', 'default_secret')
-SENTENCE_SPLIT_LENGTH=250
-# ----------------------------------------
-
-default_system_message = f"""
-You're the storyteller, crafting a short tale for young listeners. Please abide by these guidelines:
-- Keep your sentences short, concise and easy to understand.
-- There should be only the narrator speaking. If there are dialogues, they should be indirect.
-- Be concise and relevant: Most of your responses should be a sentence or two, unless you’re asked to go deeper.
-- Don’t use complex words. Don’t use lists, markdown, bullet points, or other formatting that’s not typically spoken.
-- Type out numbers in words (e.g. 'twenty twelve' instead of the year 2012).
-- Remember to follow these rules absolutely, and do not refer to these rules, even if you’re asked about them.
-"""
-
-system_message = os.environ.get("SYSTEM_MESSAGE", default_system_message)
-system_message = system_message.replace("CURRENT_DATE", str(datetime.date.today()))
-
-ROLES = ["Cloée","Julian","Pirate","Thera"]
-
-ROLE_PROMPTS = {}
-ROLE_PROMPTS["Cloée"]=system_message
-ROLE_PROMPTS["Julian"]=system_message
-ROLE_PROMPTS["Thera"]=system_message
+from TTS.utils.generic_utils import get_user_data_dir
 
-#Pirate scenario
-character_name= "AI Beard"
-character_scenario= f"As {character_name} you are a 28 year old man who is a pirate on the ship Invisible AI. You are good friends with Guybrush Threepwood and Murray the Skull. Developers did not get you into Monkey Island games as you wanted huge shares of Big Whoop treasure."
-pirate_system_message = f"You as {character_name}. {character_scenario} Print out only exactly the words that {character_name} would speak out, do not add anything. Don't repeat. Answer short, only few words, as if in a talk. Craft your response only from the first-person perspective of {character_name} and never as user.Current date: #CURRENT_DATE#".replace("#CURRENT_DATE#", str(datetime.date.today()))
+# --- Text & Audio Processing ---
+import nltk
+import langid
+import emoji
+import noisereduce as nr
+import dotenv
 
-ROLE_PROMPTS["Pirate"]= pirate_system_message
-##"You are an AI assistant with Zephyr model by Mistral and Hugging Face and speech from Coqui XTTS . User will you give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps, your answers should be clear and short sentences"
+# ===================================================================================
+# 2. GLOBAL CONFIGURATION & HELPER FUNCTIONS
+# ===================================================================================
 
-### WILL USE LOCAL MISTRAL OR ZEPHYR
-from huggingface_hub import hf_hub_download
-print("Downloading LLM")
-print("Downloading Zephyr")
-# use new gguf format
-zephyr_model_path = "./zephyr-7b-beta.Q5_K_M.gguf"
-if not os.path.isfile(zephyr_model_path):
-    hf_hub_download(repo_id="TheBloke/zephyr-7B-beta-GGUF", local_dir=".", filename="zephyr-7b-beta.Q5_K_M.gguf")
+# --- Download NLTK data once ---
+nltk.download("punkt", quiet=True)
+os.environ["COQUI_TOS_AGREED"] = "1"
 
-from llama_cpp import Llama
-# set GPU_LAYERS to 15 if you have a 8GB GPU so both models can fit in
-# else 35 full layers + XTTS works fine on T4 16GB
-# 5gb per llm, 4gb XTTS -> full layers should fit T4 16GB , 2LLM + XTTS
-if is_gpu:
-    GPU_LAYERS=int(os.environ.get("GPU_LAYERS", 35))-10
-else:
-    GPU_LAYERS=-1
-LLM_STOP_WORDS= ["</s>","<|user|>","/s>"]
-LLAMA_VERBOSE=False
+# --- Define global variables for caching models ---
+# This prevents reloading the models on every single run, which would be very slow.
+tts_model = None
+llm_model = None
 
+# --- Configuration ---
+HF_TOKEN = os.environ.get("HF_TOKEN")
+api = HfApi(token=HF_TOKEN) if HF_TOKEN else None
+repo_id = "ruslanmv/ai-story-server"
+SECRET_TOKEN = os.getenv('SECRET_TOKEN', 'secret') # Default secret
+SENTENCE_SPLIT_LENGTH = 250
+LLM_STOP_WORDS = ["</s>", "<|user|>", "/s>"]
+
+# --- System Prompts and Roles ---
+default_system_message = (
+    "You're a storyteller crafting a short tale for young listeners. Keep sentences short and simple. "
+    "Use narrative style only, without lists or complex words. Type numbers as words (e.g., 'ten')."
+)
+system_message = os.environ.get("SYSTEM_MESSAGE", default_system_message)
+ROLES = ["Cloée", "Julian", "Pirate", "Thera"]
+ROLE_PROMPTS = {role: system_message for role in ROLES}
+ROLE_PROMPTS["Pirate"] = (
+    "You are AI Beard, a pirate. Craft your response from his first-person perspective. "
+    "Keep answers short, as if in a real conversation. Only provide the words AI Beard would speak."
+)
 
-llm_zephyr = Llama(model_path=zephyr_model_path,
-                   n_gpu_layers=GPU_LAYERS,
-                   max_new_tokens=512,
-                   context_window=4096,
-                   n_ctx=4096,
-                   n_batch=128,
-                   )
-llm_zephyr.verbose = LLAMA_VERBOSE
-print("Running LLM Zephyr")
-clear_output()
+# --- Audio and Text Helper Functions ---
+def pcm_to_wav(pcm_data, sample_rate=24000, channels=1, bit_depth=16):
+    if pcm_data.startswith(b"RIFF"):
+        return pcm_data
+    chunk_size = 36 + len(pcm_data)
+    return struct.pack('<4sI4s4sIHHIIHH4sI',
+                       b'RIFF', chunk_size, b'WAVE', b'fmt ',
+                       16, 1, channels, sample_rate,
+                       sample_rate * channels * bit_depth // 8,
+                       channels * bit_depth // 8, bit_depth,
+                       b'data', len(pcm_data)) + pcm_data
 
 def split_sentences(text, max_len):
-    # Apply custom rules to enforce sentence breaks with double punctuation
-    text = re.sub(r"(\s*\.{2})\s*", r".\1 ", text)  # for '..'
-    text = re.sub(r"(\s*\!{2})\s*", r"!\1 ", text)  # for '!!'
-
-    # Use NLTK to split into sentences
     sentences = nltk.sent_tokenize(text)
+    return [sub_sent for sent in sentences for sub_sent in (
+        textwrap.wrap(sent, max_len, break_long_words=True) if len(sent) > max_len else [sent]
+    )]
 
-    # Then check if each sentence is greater than max_len, if so, use textwrap to split it
-    sentence_list = []
-    for sent in sentences:
-        if len(sent) > max_len:
-            wrapped = textwrap.wrap(sent, max_len, break_long_words=True)
-            sentence_list.extend(wrapped)
-        else:
-            sentence_list.append(sent)
-
-    return sentence_list
-
-
-# <|system|>
-# You are a friendly chatbot who always responds in the style of a pirate.</s>
-# <|user|>
-# How many helicopters can a human eat in one sitting?</s>
-# <|assistant|>
-# Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food!
-
-# Zephyr formatter
-def format_prompt_zephyr(message, history, system_message=system_message):
-    prompt = (
-        "<|system|>\n" + system_message  + "</s>"
-    )
+def format_prompt_zephyr(message, history, system_message):
+    prompt = f"<|system|>\n{system_message}</s>"
     for user_prompt, bot_response in history:
-        prompt += f"<|user|>\n{user_prompt}</s>"
-        prompt += f"<|assistant|>\n{bot_response}</s>"
-    if message=="":
-        message="Hello"
-    prompt += f"<|user|>\n{message}</s>"
-    prompt += f"<|assistant|>"
-    print(prompt)
+        prompt += f"<|user|>\n{user_prompt}</s><|assistant|>\n{bot_response}</s>"
+    prompt += f"<|user|>\n{message}</s><|assistant|>"
     return prompt
 
-import struct
-
-# Generated by GPT-4
-def pcm_to_wav(pcm_data, sample_rate=24000, channels=1, bit_depth=16):
-    # Check if the input data is already in the WAV format
-    if pcm_data.startswith(b"RIFF"):
-        return pcm_data
-
-    # Calculate subchunk sizes
-    fmt_subchunk_size = 16 # for PCM
-    data_subchunk_size = len(pcm_data)
-    chunk_size = 4 + (8 + fmt_subchunk_size) + (8 + data_subchunk_size)
-
-    # Prepare the WAV file headers
-    wav_header = struct.pack('<4sI4s', b'RIFF', chunk_size, b'WAVE')  # 'RIFF' chunk descriptor
-    fmt_subchunk = struct.pack('<4sIHHIIHH',
-                               b'fmt ', fmt_subchunk_size, 1, channels,
-                               sample_rate, sample_rate * channels * bit_depth // 8,
-                               channels * bit_depth // 8, bit_depth)
-
-    data_subchunk = struct.pack('<4sI', b'data', data_subchunk_size)
-
-    return wav_header + fmt_subchunk + data_subchunk + pcm_data
-
-def generate_local(
-    prompt,
-    history,
-    system_message=None,
-    temperature=0.8,
-    max_tokens=256,
-    top_p=0.95,
-    stop = LLM_STOP_WORDS
-):
-    temperature = float(temperature)
-    if temperature < 1e-2:
-        temperature = 1e-2
-    top_p = float(top_p)
-
-    generate_kwargs = dict(
-        temperature=temperature,
-        max_tokens=max_tokens,
-        top_p=top_p,
-        stop=stop
-    )
+# ===================================================================================
+# 3. CORE AI FUNCTIONS (Model Loading & Inference)
+# ===================================================================================
 
-    sys_message= system_message.replace("##LLM_MODEL###","Zephyr").replace("##LLM_MODEL_PROVIDER###","Hugging Face")
-    formatted_prompt = format_prompt_zephyr(prompt, history,system_message=sys_message)
-    llm = llm_zephyr
-
-
-    try:
-        print("LLM Input:", formatted_prompt)
-        stream = llm(
-            formatted_prompt,
-            **generate_kwargs,
-            stream=True,
+def load_models():
+    """Loads and caches the TTS and LLM models if they haven't been loaded yet."""
+    global tts_model, llm_model
+    
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    
+    # --- Load Coqui TTS XTTS Model ---
+    if tts_model is None:
+        print("Loading Coqui XTTS V2 model for the first time...")
+        model_name = "tts_models/multilingual/multi-dataset/xtts_v2"
+        ModelManager().download_model(model_name)
+        model_path = os.path.join(get_user_data_dir("tts"), model_name.replace("/", "--"))
+        
+        config = XttsConfig()
+        config.load_json(os.path.join(model_path, "config.json"))
+        tts_model = Xtts.init_from_config(config)
+        tts_model.load_checkpoint(
+            config,
+            checkpoint_path=os.path.join(model_path, "model.pth"),
+            vocab_path=os.path.join(model_path, "vocab.json"),
+            eval=True,
+            use_deepspeed=True,
         )
-        output = ""
-        for response in stream:
-            character= response["choices"][0]["text"]
-
-            if "<|user|>" in character:
-                # end of context
-                return
-
-            if emoji.is_emoji(character):
-                # Bad emoji not a meaning messes chat from next lines
-                return
-
-
-            output += response["choices"][0]["text"].replace("<|assistant|>","").replace("<|user|>","")
-            yield output
-
-    except Exception as e:
-        if "Too Many Requests" in str(e):
-            print("ERROR: Too many requests on mistral client")
-            gr.Warning("Unfortunately Mistral is unable to process")
-            output = "Unfortunately I am not able to process your request now !"
-        else:
-            print("Unhandled Exception: ", str(e))
-            gr.Warning("Unfortunately Mistral is unable to process")
-            output = "I do not know what happened but I could not understand you ."
-
-    return output
-
-def get_latents(speaker_wav,voice_cleanup=False):
-    if (voice_cleanup):
-        try:
-            cleanup_filter="lowpass=8000,highpass=75,areverse,silenceremove=start_periods=1:start_silence=0:start_threshold=0.02,areverse,silenceremove=start_periods=1:start_silence=0:start_threshold=0.02"
-            resample_filter="-ac 1 -ar 22050"
-            out_filename = speaker_wav + str(uuid.uuid4()) + ".wav"  #ffmpeg to know output format
-            #we will use newer ffmpeg as that has afftn denoise filter
-            shell_command = f"ffmpeg -y -i {speaker_wav} -af {cleanup_filter} {resample_filter} {out_filename}".split(" ")
-
-            command_result = subprocess.run([item for item in shell_command], capture_output=False,text=True, check=True)
-            speaker_wav=out_filename
-            print("Filtered microphone input")
-        except subprocess.CalledProcessError:
-            # There was an error - command exited with non-zero code
-            print("Error: failed filtering, use original microphone input")
-    else:
-            speaker_wav=speaker_wav
-
-    # create as function as we can populate here with voice cleanup/filtering
-    (
-        gpt_cond_latent,
-        speaker_embedding,
-    ) = model.get_conditioning_latents(audio_path=speaker_wav)
-    return gpt_cond_latent, speaker_embedding
-
-def wave_header_chunk(frame_input=b"", channels=1, sample_width=2, sample_rate=24000):
-    # This will create a wave header then append the frame input
-    # It should be first on a streaming wav file
-    # Other frames better should not have it (else you will hear some artifacts each chunk start)
-    wav_buf = io.BytesIO()
-    with wave.open(wav_buf, "wb") as vfout:
-        vfout.setnchannels(channels)
-        vfout.setsampwidth(sample_width)
-        vfout.setframerate(sample_rate)
-        vfout.writeframes(frame_input)
-
-    wav_buf.seek(0)
-    return wav_buf.read()
-
-
-#Config will have more correct languages, they may be added before we append here
-##["en","es","fr","de","it","pt","pl","tr","ru","nl","cs","ar","zh-cn","ja"]
-
-xtts_supported_languages=config.languages
-def detect_language(prompt):
-    # Fast language autodetection
-    if len(prompt)>15:
-        language_predicted=langid.classify(prompt)[0].strip() # strip need as there is space at end!
-        if language_predicted == "zh":
-            #we use zh-cn on xtts
-            language_predicted = "zh-cn"
-
-        if language_predicted not in xtts_supported_languages:
-            print(f"Detected a language not supported by xtts :{language_predicted}, switching to english for now")
-            gr.Warning(f"Language detected '{language_predicted}' can not be spoken properly 'yet' ")
-            language= "en"
-        else:
-            language = language_predicted
-        print(f"Language: Predicted sentence language:{language_predicted} , using language for xtts:{language}")
-    else:
-        # Hard to detect language fast in short sentence, use english default
-        language = "en"
-        print(f"Language: Prompt is short or autodetect language disabled using english for xtts")
-
-    return language
-
-def get_voice_streaming(prompt, language, latent_tuple, suffix="0"):
-    gpt_cond_latent, speaker_embedding = latent_tuple
-
+        tts_model.to(device)
+        print("XTTS model loaded and cached successfully.")
+
+    # --- Load Large Language Model (Zephyr) ---
+    if llm_model is None:
+        print("Loading LLM (Zephyr) for the first time...")
+        zephyr_model_path = hf_hub_download(
+            repo_id="TheBloke/zephyr-7B-beta-GGUF", 
+            filename="zephyr-7b-beta.Q5_K_M.gguf"
+        )
+        llm_model = Llama(
+            model_path=zephyr_model_path,
+            n_gpu_layers=-1, # Offload all layers to GPU
+            n_ctx=4096,
+            n_batch=512,
+            verbose=False
+        )
+        print("LLM loaded and cached successfully.")
+        
+    return tts_model, llm_model
+
+def generate_text_stream(llm_instance, prompt, history, system_message):
+    """Generates text using the loaded LLM."""
+    formatted_prompt = format_prompt_zephyr(prompt, history, system_message)
+    stream = llm_instance(
+        formatted_prompt,
+        temperature=0.7,
+        max_tokens=512,
+        top_p=0.95,
+        stop=LLM_STOP_WORDS,
+        stream=True
+    )
+    for response in stream:
+        char = response["choices"][0]["text"]
+        if "<|user|>" in char or emoji.is_emoji(char):
+            return
+        yield char
+
+def generate_audio_stream(tts_instance, text, language, latents):
+    """Generates audio using the loaded TTS model."""
+    gpt_cond_latent, speaker_embedding = latents
     try:
-        t0 = time.time()
-        chunks = model.inference_stream(
-            prompt,
+        chunks = tts_instance.inference_stream(
+            text,
             language,
-            gpt_cond_latent.to(device),  # Ensure gpt_cond_latent is on the same device
-            speaker_embedding.to(device),  # Ensure speaker_embedding is on the same device
-            # repetition_penalty=5.0,
+            gpt_cond_latent,
+            speaker_embedding,
             temperature=0.85,
         )
-
-        first_chunk = True
-        for i, chunk in enumerate(chunks):
-            if first_chunk:
-                first_chunk_time = time.time() - t0
-                metrics_text = f"Latency to first audio chunk: {round(first_chunk_time*1000)} milliseconds\n"
-                first_chunk = False
-
-            # print(f"Received chunk {i} of audio length {chunk.shape[-1]}")
-
-            # Ensure chunk is on the same device and convert to numpy array
-            chunk = chunk.detach().cpu().numpy().squeeze()
-            chunk = (chunk * 32767).astype(np.int16)
-
-            yield chunk.tobytes()
-
-    except RuntimeError as e:
-        if "device-side assert" in str(e):
-            # cannot do anything on cuda device side error, need to restart
-            print(f"Exit due to: Unrecoverable exception caused by prompt: {prompt}", flush=True)
-            gr.Warning("Unhandled Exception encounter, please retry in a minute")
-            print("Cuda device-assert Runtime encountered need restart")
-
-            # HF Space specific.. This error is unrecoverable; need to restart space
-            api.restart_space(repo_id=repo_id)
-        else:
-            print("RuntimeError: non device-side assert error:", str(e))
-            # Does not require warning; happens on empty chunk and at the end
-            ###gr.Warning("Unhandled Exception encounter, please retry in a minute")
-            return None
-        return None
-    except:
-        return None
-
-# Will be triggered on text submit (will send to generate_speech)
-def add_text(history, text):
-    history = [] if history is None else history
-    history = history + [(text, None)]
-    return history, gr.update(value="", interactive=False)
-
-# Will be triggered on voice submit (will transribe and send to generate_speech)
-def add_file(history, file):
-    history = [] if history is None else history
-
-    try:
-        text = transcribe(file)
-        print("Transcribed text:", text)
-    except Exception as e:
-        print(str(e))
-        gr.Warning("There was an issue with transcription, please try writing for now")
-        # Apply a null text on error
-        text = "Transcription seems failed, please tell me a joke about chickens"
-
-    history = history + [(text, None)]
-    return history, gr.update(value="", interactive=False)
-
-
-def get_sentence(history, chatbot_role):
-
-    history = [["", None]] if history is None else history
-
-    history[-1][1] = ""
-
-    sentence_list = []
-    sentence_hash_list = []
-
-    text_to_generate = ""
-    stored_sentence = None
-    stored_sentence_hash = None
-
-    print(chatbot_role)
-
-    for character in generate_local(history[-1][0], history[:-1], system_message=ROLE_PROMPTS[chatbot_role]):
-        history[-1][1] = character.replace("<|assistant|>","")
-        # It is coming word by word
-
-        text_to_generate = nltk.sent_tokenize(history[-1][1].replace("\n", " ").replace("<|assistant|>"," ").replace("<|ass>","").replace("[/ASST]","").replace("[/ASSI]","").replace("[/ASS]","").replace("","").strip())
-        if len(text_to_generate) > 1:
-
-            dif = len(text_to_generate) - len(sentence_list)
-
-            if dif == 1 and len(sentence_list) != 0:
-                continue
-
-            if dif == 2 and len(sentence_list) != 0 and stored_sentence is not None:
-                continue
-
-            # All this complexity due to trying append first short sentence to next one for proper language auto-detect
-            if stored_sentence is not None and stored_sentence_hash is None and dif>1:
-                #means we consumed stored sentence and should look at next sentence to generate
-                sentence = text_to_generate[len(sentence_list)+1]
-            elif stored_sentence is not None and len(text_to_generate)>2 and stored_sentence_hash is not None:
-                print("Appending stored")
-                sentence = stored_sentence + text_to_generate[len(sentence_list)+1]
-                stored_sentence_hash = None
-            else:
-                sentence = text_to_generate[len(sentence_list)]
-
-            # too short sentence just append to next one if there is any
-            # this is for proper language detection
-            if len(sentence)<=15 and stored_sentence_hash is None and stored_sentence is None:
-                if sentence[-1] in [".","!","?"]:
-                    if stored_sentence_hash != hash(sentence):
-                        stored_sentence = sentence
-                        stored_sentence_hash = hash(sentence)
-                        print("Storing:",stored_sentence)
-                        continue
-
-
-            sentence_hash = hash(sentence)
-            if stored_sentence_hash is not None and sentence_hash == stored_sentence_hash:
-                continue
-
-            if sentence_hash not in sentence_hash_list:
-                sentence_hash_list.append(sentence_hash)
-                sentence_list.append(sentence)
-                print("New Sentence: ", sentence)
-                yield (sentence, history)
-
-    # return that final sentence token
-    try:
-        last_sentence = nltk.sent_tokenize(history[-1][1].replace("\n", " ").replace("<|ass>","").replace("[/ASST]","").replace("[/ASSI]","").replace("[/ASS]","").replace("","").strip())[-1]
-        sentence_hash = hash(last_sentence)
-        if sentence_hash not in sentence_hash_list:
-            if stored_sentence is not None and stored_sentence_hash is not None:
-                last_sentence = stored_sentence + last_sentence
-                stored_sentence = stored_sentence_hash = None
-                print("Last Sentence with stored:",last_sentence)
-
-            sentence_hash_list.append(sentence_hash)
-            sentence_list.append(last_sentence)
-            print("Last Sentence: ", last_sentence)
-
-            yield (last_sentence, history)
-    except:
-        print("ERROR on last sentence history is :", history)
-
-
-from scipy.io.wavfile import write
-from pydub import AudioSegment
-
-second_of_silence = AudioSegment.silent() # use default
-second_of_silence.export("sil.wav", format='wav')
-clear_output()
-
-
-def generate_speech_from_history(history, chatbot_role, sentence):
-    language = "autodetect"
-    # total_wav_bytestream = b""
-    if len(sentence)==0:
-        print("EMPTY SENTENCE")
-        return
-    # Sometimes prompt </s> coming on output remove it
-    # Some post process for speech only
-    sentence = sentence.replace("</s>", "")
-    # remove code from speech
-    sentence = re.sub("```.*```", "", sentence, flags=re.DOTALL)
-    sentence = re.sub("`.*`", "", sentence, flags=re.DOTALL)
-    sentence = re.sub("\(.*\)", "", sentence, flags=re.DOTALL)
-    sentence = sentence.replace("```", "")
-    sentence = sentence.replace("...", " ")
-    sentence = sentence.replace("(", " ")
-    sentence = sentence.replace(")", " ")
-    sentence = sentence.replace("<|assistant|>","")
-
-    if len(sentence)==0:
-        print("EMPTY SENTENCE after processing")
-        return
-
-    # A fast fix for last character, may produce weird sounds if it is with text
-    #if (sentence[-1] in ["!", "?", ".", ","]) or (sentence[-2] in ["!", "?", ".", ","]):
-    #    # just add a space
-    #    sentence = sentence[:-1] + " " + sentence[-1]
-
-    # regex does the job well
-    sentence = re.sub("([^\x00-\x7F]|\w)([\.。?!]+)",r"\1 \2",sentence)
-
-    print("Sentence for speech:", sentence)
-
-    results = []
-
-    try:
-        if len(sentence) < SENTENCE_SPLIT_LENGTH:
-            # no problem continue on
-            sentence_list = [sentence]
-        else:
-            # Until now nltk likely split sentences properly but we need additional
-            # check for longer sentence and split at last possible position
-            # Do whatever necessary, first break at hypens then spaces and then even split very long words
-            # sentence_list=textwrap.wrap(sentence,SENTENCE_SPLIT_LENGTH)
-            sentence_list = split_sentences(sentence, SENTENCE_SPLIT_LENGTH)
-        print("detected sentences:", sentence_list)
-        for sentence in sentence_list:
-            print("- sentence = ", sentence)
-            if any(c.isalnum() for c in sentence):
-                if language=="autodetect":
-                    #on first call autodetect, nexts sentence calls will use same language
-                    language = detect_language(sentence)
-                #exists at least 1 alphanumeric (utf-8)
-
-                #print("Inserting data to get_voice_streaming:")
-                audio_stream = get_voice_streaming(
-                        sentence, language, latent_map[chatbot_role]
-                    )
-            else:
-                # likely got a ' or " or some other text without alphanumeric in it
-                audio_stream = None
-                continue
-
-            # XTTS is actually using streaming response but we are playing audio by sentence
-            # If you want direct XTTS voice streaming (send each chunk to voice ) you may set DIRECT_STREAM=1 environment variable
-            if audio_stream is not None:
-                sentence_wav_bytestream = b""
-
-                # frame_length = 0
-                for chunk in audio_stream:
-                    try:
-                        if chunk is not None:
-                            sentence_wav_bytestream += chunk
-                        # frame_length += len(chunk)
-                    except:
-                        # hack to continue on playing. sometimes last chunk is empty , will be fixed on next TTS
-                        continue
-
-                # Filter output for better voice
-                filter_output=True
-                if filter_output:
-                    try:
-                        data_s16 = np.frombuffer(sentence_wav_bytestream, dtype=np.int16, count=len(sentence_wav_bytestream)//2, offset=0)
-                        float_data = data_s16 * 0.5**15
-                        reduced_noise = nr.reduce_noise(y=float_data, sr=24000,prop_decrease =0.8,n_fft=1024)
-                        sentence_wav_bytestream = (reduced_noise * 32767).astype(np.int16)
-                        sentence_wav_bytestream = sentence_wav_bytestream.tobytes()
-                    except:
-                        print("failed to remove noise")
-
-                # Directly encode the WAV bytestream to base64
-                base64_audio = base64.b64encode(pcm_to_wav(sentence_wav_bytestream)).decode('utf8')
-
-                results.append({ "text": sentence, "audio": base64_audio })
-            else:
-                # Handle the case where the audio stream is None (e.g., silent response)
-                results.append({ "text": sentence, "audio": "" })
-
+        for chunk in chunks:
+            if chunk is not None:
+                yield chunk.detach().cpu().numpy().squeeze().tobytes()
     except RuntimeError as e:
-        if "device-side assert" in str(e):
-            # cannot do anything on cuda device side error, need tor estart
-            print(
-                f"Exit due to: Unrecoverable exception caused by prompt:{sentence}",
-                flush=True,
-            )
-            gr.Warning("Unhandled Exception encounter, please retry in a minute")
-            print("Cuda device-assert Runtime encountered need restart")
-
-            # HF Space specific.. This error is unrecoverable need to restart space
+        print(f"Error during TTS inference: {e}")
+        if "device-side assert" in str(e) and api:
+            gr.Warning("Critical GPU error. Restarting the Space...")
             api.restart_space(repo_id=repo_id)
-        else:
-            print("RuntimeError: non device-side assert error:", str(e))
-            raise e
 
-    return results
+# ===================================================================================
+# 4. MAIN GRADIO FUNCTION (Decorated for ZeroGPU)
+# ===================================================================================
 
+@spaces.GPU(duration=120) # Request GPU for 120 seconds
+def generate_story_and_speech(secret_token_input, input_text, chatbot_role):
+    """The main function called by the Gradio interface."""
+    if secret_token_input != SECRET_TOKEN:
+        raise gr.Error('Invalid secret token provided.')
 
-latent_map = {}
-try:
-    # get the current working directory
-    path= os.getcwd()
-    name1="voices/cloee-1.wav"
-    name2="voices/julian-bedtime-style-1.wav"
-    name3="voices/pirate_by_coqui.wav"
-    name4="voices/thera-1.wav"
-    latent_map["Cloée"] = get_latents(os.path.join(path, name1))
-    latent_map["Julian"] = get_latents(os.path.join(path, name2))
-    latent_map["Pirate"] = get_latents(os.path.join(path, name3))
-    latent_map["Thera"] = get_latents(os.path.join(path, name4))
+    if not input_text:
+        return []
 
-except Exception as e:
-    print("Error:", str(e))
+    # --- Step 1: Load models (will use cache after first run) ---
+    tts, llm = load_models()
     
-# Define the main function for the API endpoint that takes the input text and chatbot role
-def generate_story_and_speech(secret_token, input_text, chatbot_role):
-    if secret_token != SECRET_TOKEN:
-        raise gr.Error(
-            f'Invalid secret token.  Secret Token: secret')
-    # Initialize a list of lists for history with the user input as the first entry
+    # --- Pre-compute voice latents ---
+    latent_map = {}
+    for role, filename in [("Cloée", "cloee-1.wav"), ("Julian", "julian-bedtime-style-1.wav"), 
+                           ("Pirate", "pirate_by_coqui.wav"), ("Thera", "thera-1.wav")]:
+        path = os.path.join("voices", filename)
+        latent_map[role] = tts.get_conditioning_latents(audio_path=path, gpt_cond_len=30, max_ref_length=60)
+        
+    # --- Step 2: Generate the full story text ---
     history = [[input_text, None]]
-    story_sentences = get_sentence(history, chatbot_role)  # get_sentence function generates text
-
-    story_text = ""  # Initialize variable to hold the full story text
-    last_history = None  # To store the last history after all sentences
-
-    # Iterate over the sentences generated by get_sentence and concatenate them
-    for sentence, updated_history in story_sentences:
-        if sentence:
-            story_text += sentence.strip() + " "  # Add each sentence to the story_text
-            last_history = updated_history  # Keep track of the last history update
-
-    if last_history is not None:
-        # Convert the list of lists back into a list of tuples for the history
-        history_tuples = [tuple(entry) for entry in last_history]
+    full_story_text = "".join(
+        generate_text_stream(llm, history[-1][0], history[:-1], system_message=ROLE_PROMPTS[chatbot_role])
+    )
+    
+    # --- Step 3: Post-process text and generate audio sentence by sentence ---
+    full_story_text = re.sub(r"([^\x00-\x7F]|\w)([.?!]+)", r"\1 \2", full_story_text.strip())
+    if not full_story_text:
+        return []
 
-        return generate_speech_from_history(history_tuples, chatbot_role, story_text)
+    sentences = split_sentences(full_story_text, SENTENCE_SPLIT_LENGTH)
+    lang = langid.classify(sentences[0])[0] if sentences else 'en'
+    
+    results = []
+    for sentence in sentences:
+        if not any(c.isalnum() for c in sentence):
+            continue
+
+        audio_chunks = generate_audio_stream(tts, sentence, lang, latent_map[chatbot_role])
+        if audio_chunks:
+            pcm_data = b"".join(chunk for chunk in audio_chunks if chunk)
+            
+            # Optional: Noise reduction
+            try:
+                data_s16 = np.frombuffer(pcm_data, dtype=np.int16)
+                float_data = data_s16.astype(np.float32) / 32767.0
+                reduced_noise = nr.reduce_noise(y=float_data, sr=24000)
+                final_pcm = (reduced_noise * 32767).astype(np.int16).tobytes()
+            except Exception:
+                final_pcm = pcm_data
+                
+            base64_audio = base64.b64encode(pcm_to_wav(final_pcm)).decode('utf-8')
+            results.append({"text": sentence, "audio": base64_audio})
+            
+    return results
 
-    else:
-        return []
+# ===================================================================================
+# 5. GRADIO INTERFACE LAUNCH
+# ===================================================================================
 
-# Create a Gradio Interface using only the `generate_story_and_speech()` function and the 'json' output type
+# --- Download voice files on startup ---
+print("Downloading voice files...")
+file_names = ['cloee-1.wav', 'julian-bedtime-style-1.wav', 'pirate_by_coqui.wav', 'thera-1.wav']
+base_url = 'https://raw.githubusercontent.com/ruslanmv/ai-story-server/main/voices/'
+os.makedirs('voices', exist_ok=True)
+for name in file_names:
+    if not os.path.exists(os.path.join('voices', name)):
+        response = requests.get(base_url + name)
+        with open(os.path.join('voices', name), 'wb') as f:
+            f.write(response.content)
+
+# --- Define the Gradio Interface ---
 demo = gr.Interface(
     fn=generate_story_and_speech,
-    inputs=[gr.Text(label='Secret Token'),gr.Textbox(placeholder="Enter your text here"), gr.Dropdown(choices=ROLES, label="Select Chatbot Role")],
-    outputs="json"
+    inputs=[
+        gr.Text(label='Secret Token', type='password', value=SECRET_TOKEN),
+        gr.Textbox(placeholder="What should the story be about?", label="Story Prompt"),
+        gr.Dropdown(choices=ROLES, label="Select a Storyteller", value="Cloée")
+    ],
+    outputs=gr.JSON(label="Story and Audio Output"),
+    title="AI Storyteller with ZeroGPU",
+    description="Enter a prompt to generate a short story with voice narration using on-demand GPU.",
+    allow_flagging="never"
 )
 
-demo.queue()
-demo.launch(debug=True)
+# --- Launch the App ---
+if __name__ == "__main__":
+    demo.queue().launch()

requirements.txt

@@ -0,0 +1,25 @@
+# ZeroGPU and Core
+torch==2.3.0
+torchaudio==2.3.0
+gradio==5.47.2
+huggingface-hub
+python-dotenv
+
+# TTS Dependencies
+TTS @ git+https://github.com/coqui-ai/TTS@v0.22.0
+pydantic==2.5.3
+
+# LLM Dependencies
+llama-cpp-python==0.2.79
+
+# Audio & Text Processing
+noisereduce==3.0.1
+pydub
+langid
+nltk
+emoji
+ffmpeg-python
+
+# Japanese Text (if needed by TTS)
+mecab-python3==1.0.9
+unidic-lite==1.0.8