app.py

import gradio as gr
import google.generativeai as genai
import numpy as np
import re
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from huggingface_hub import snapshot_download, login
import logging
import os
import spaces
import warnings
from snac import SNAC
from dotenv import load_dotenv

load_dotenv()

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

warnings.filterwarnings("ignore", category=UserWarning)
warnings.filterwarnings("ignore", category=RuntimeWarning)

device = "cuda" if torch.cuda.is_available() else "cpu"
logger.info(f"Using device: {device}")

model = None
tokenizer = None
snac_model = None

@spaces.GPU()
def load_model():
    global model, tokenizer, snac_model
    try:
        logger.info("Loading SNAC model...")
        snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz")
        snac_model = snac_model.to(device)
        
        if snac_model is None:
            raise ValueError("Failed to load SNAC model")
        
        logger.info("Loading Orpheus model...")
        model_name = "canopylabs/orpheus-3b-0.1-ft"

        hf_token = os.environ.get("HUGGINGFACE_TOKEN")
        if not hf_token:
            raise ValueError("HUGGINGFACE_TOKEN environment variable is not set")

        login(token=hf_token)

        snapshot_download(
            repo_id=model_name,
            use_auth_token=hf_token,
            allow_patterns=["config.json", "*.safetensors", "model.safetensors.index.json", "vocab.json", "merges.txt", "tokenizer.json"],
            ignore_patterns=["optimizer.pt", "pytorch_model.bin", "training_args.bin", "scheduler.pt"]
        )

        model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
        model.to(device)
        
        tokenizer = AutoTokenizer.from_pretrained(model_name)
        if tokenizer is None:
            raise ValueError("Failed to load tokenizer")
        
        logger.info(f"Orpheus model and tokenizer loaded to {device}")
    except Exception as e:
        logger.error(f"Error loading model: {str(e)}")
        raise

@spaces.GPU()
def generate_podcast_script(api_key, content, uploaded_file, duration, num_hosts):
    try:
        genai.configure(api_key=api_key)
        model = genai.GenerativeModel('gemini-2.5-pro-preview-03-25')
        
        combined_content = content or ""
        if uploaded_file:
            file_content = uploaded_file.read().decode('utf-8')
            combined_content += "\n" + file_content if combined_content else file_content
        
        prompt = f"""
        Create a podcast script for {'one person' if num_hosts == 1 else 'two people'} discussing:
        {combined_content}
        
        Duration: {duration}. Include natural speech, humor, and occasional off-topic thoughts.
        Use speech fillers like um, ah. Vary emotional tone.
        
        Format: {'Monologue' if num_hosts == 1 else 'Alternating dialogue'} without speaker labels.
        Separate {'paragraphs' if num_hosts == 1 else 'lines'} with blank lines.
        
        Use emotion tags in angle brackets: <laugh>, <sigh>, <chuckle>, <cough>, <sniffle>, <groan>, <yawn>, <gasp>.
        
        Example: "I can't believe I stayed up all night <yawn> only to find out the meeting was canceled <groan>."
        
        Ensure content flows naturally and stays on topic. Match the script length to {duration}.
        """
        
        response = model.generate_content(prompt)
        return re.sub(r'[^a-zA-Z0-9\s.,?!<>]', '', response.text)
    except Exception as e:
        logger.error(f"Error generating podcast script: {str(e)}")
        raise

def process_prompt(prompt, voice, tokenizer, device):
    prompt = f"{voice}: {prompt}"
    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
    
    start_token = torch.tensor([[128259]], dtype=torch.int64)
    end_tokens = torch.tensor([[128009, 128260]], dtype=torch.int64)
    
    modified_input_ids = torch.cat([start_token, input_ids, end_tokens], dim=1)
    attention_mask = torch.ones_like(modified_input_ids)
    
    return modified_input_ids.to(device), attention_mask.to(device)

def parse_output(generated_ids):
    token_to_find = 128257
    token_to_remove = 128258
    
    token_indices = (generated_ids == token_to_find).nonzero(as_tuple=True)

    if len(token_indices[1]) > 0:
        last_occurrence_idx = token_indices[1][-1].item()
        cropped_tensor = generated_ids[:, last_occurrence_idx+1:]
    else:
        cropped_tensor = generated_ids

    processed_rows = []
    for row in cropped_tensor:
        masked_row = row[row != token_to_remove]
        processed_rows.append(masked_row)

    code_lists = []
    for row in processed_rows:
        row_length = row.size(0)
        new_length = (row_length // 7) * 7
        trimmed_row = row[:new_length]
        trimmed_row = [t - 128266 for t in trimmed_row]
        code_lists.append(trimmed_row)
        
    return code_lists[0]

def redistribute_codes(code_list, snac_model):
    try:
        device = next(snac_model.parameters()).device
        
        layer_1, layer_2, layer_3 = [], [], []
        for i in range((len(code_list)+1)//7):
            layer_1.append(code_list[7*i])
            layer_2.append(code_list[7*i+1]-4096)
            layer_3.append(code_list[7*i+2]-(2*4096))
            layer_3.append(code_list[7*i+3]-(3*4096))
            layer_2.append(code_list[7*i+4]-(4*4096))
            layer_3.append(code_list[7*i+5]-(5*4096))
            layer_3.append(code_list[7*i+6]-(6*4096))
        
        codes = [
            torch.tensor(layer_1, device=device).unsqueeze(0),
            torch.tensor(layer_2, device=device).unsqueeze(0),
            torch.tensor(layer_3, device=device).unsqueeze(0)
        ]
        
        audio_hat = snac_model.decode(codes)
        return audio_hat.detach().squeeze().cpu().numpy()
    except Exception as e:
        logger.error(f"Error in redistribute_codes: {e}", exc_info=True)
        return None

@spaces.GPU()
def generate_speech(text, voice, temperature, top_p, repetition_penalty, max_new_tokens, progress=gr.Progress()):
    if not text.strip():
        logger.warning("Empty text input. Skipping speech generation.")
        return None
    
    try:
        progress(0.1, "Processing text...")
        input_ids, attention_mask = process_prompt(text, voice, tokenizer, device)
        logger.info(f"Input shape: {input_ids.shape}")
        
        progress(0.3, "Generating speech tokens...")
        with torch.no_grad():
            generated_ids = model.generate(
                input_ids=input_ids,
                attention_mask=attention_mask,
                max_new_tokens=max_new_tokens,
                do_sample=True,
                temperature=temperature,
                top_p=top_p,
                repetition_penalty=repetition_penalty,
                num_return_sequences=1,
                eos_token_id=128258,
            )
        logger.info(f"Generated shape: {generated_ids.shape}")
        
        progress(0.6, "Processing speech tokens...")
        code_list = parse_output(generated_ids)
        logger.info(f"Code list length: {len(code_list)}")
        
        if not code_list:
            logger.warning("No valid code list generated. Skipping audio conversion.")
            return None
        
        progress(0.8, "Converting to audio...")
        audio_samples = redistribute_codes(code_list, snac_model)
        
        if audio_samples is None:
            logger.warning("Audio samples is None.")
            return None
        
        if len(audio_samples) == 0:
            logger.warning("Audio samples is empty.")
            return None
        
        logger.info(f"Audio samples shape: {audio_samples.shape}")
        return (24000, audio_samples)  # Return sample rate and audio
    except Exception as e:
        logger.error(f"Error generating speech: {e}", exc_info=True)
        return None

@spaces.GPU()
def render_podcast(api_key, script, voice1, voice2, num_hosts):
    try:
        lines = [line for line in script.split('\n') if line.strip()]
        audio_segments = []

        for i, line in enumerate(lines):
            voice = voice1 if num_hosts == 1 or i % 2 == 0 else voice2
            try:
                result = generate_speech(line, voice, temperature=0.6, top_p=0.95, repetition_penalty=1.1, max_new_tokens=1200)
                if result is not None:
                    sample_rate, audio = result
                    audio_segments.append(audio)
            except Exception as e:
                logger.error(f"Error processing audio segment: {str(e)}")

        if not audio_segments:
            logger.warning("No valid audio segments were generated.")
            return (24000, np.zeros(24000, dtype=np.float32))

        podcast_audio = np.concatenate(audio_segments)
        podcast_audio = np.clip(podcast_audio, -1, 1)
        podcast_audio = (podcast_audio * 32767).astype(np.int16)
        
        return (24000, podcast_audio)
    except Exception as e:
        logger.error(f"Error rendering podcast: {str(e)}")
        raise

with gr.Blocks() as demo:
    gr.Markdown("# AI Podcast Generator")
    
    api_key_input = gr.Textbox(label="Enter your Gemini API Key", type="password")
    
    with gr.Row():
        content_input = gr.Textbox(label="Paste your content (optional)", lines=4)
        document_upload = gr.File(label="Upload Document (optional)")
    
    duration = gr.Radio(["1-5 min", "5-10 min", "10-15 min"], label="Estimated podcast duration", value="1-5 min")
    num_hosts = gr.Radio([1, 2], label="Number of podcast hosts", value=2)
    
    voice_options = ["tara", "leah", "jess", "leo", "dan", "mia", "zac", "zoe"]
    voice1_select = gr.Dropdown(label="Select Voice 1", choices=voice_options, value="tara")
    voice2_select = gr.Dropdown(label="Select Voice 2", choices=voice_options, value="leo")
    
    generate_btn = gr.Button("Generate Script")
    script_output = gr.Textbox(label="Generated Script", lines=10)
    
    render_btn = gr.Button("Render Podcast")
    audio_output = gr.Audio(label="Generated Podcast")
    
    generate_btn.click(generate_podcast_script, 
                       inputs=[api_key_input, content_input, document_upload, duration, num_hosts], 
                       outputs=script_output)
    
    render_btn.click(render_podcast, 
                     inputs=[api_key_input, script_output, voice1_select, voice2_select, num_hosts], 
                     outputs=audio_output)

    num_hosts.change(lambda x: gr.update(visible=x == 2), 
                     inputs=[num_hosts], 
                     outputs=[voice2_select])

if __name__ == "__main__":
    try:
        load_model()
        demo.queue().launch(share=False, ssr_mode=False)
    except Exception as e:
        logger.error(f"Error launching the application: {str(e)}")