app.py

import spaces
from snac import SNAC
import torch
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
from huggingface_hub import snapshot_download
import google.generativeai as genai
import re
import logging

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Check if CUDA is available
device = "cuda" if torch.cuda.is_available() else "cpu"

print("Loading SNAC model...")
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz")
snac_model = snac_model.to(device)

model_name = "canopylabs/orpheus-3b-0.1-ft"

model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
model.to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name)
print(f"Orpheus model loaded to {device}")

@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]

@spaces.GPU()
def generate_speech(text, voice, temperature, top_p, repetition_penalty, max_new_tokens, progress=gr.Progress()):
    if not text.strip():
        return None
    
    try:
        progress(0.1, "Processing text...")
        input_ids, attention_mask = process_prompt(text, voice, tokenizer, device)
        
        progress(0.3, "Generating speech tokens...")
        with torch.no_grad():
            generated_ids = model.generate(
                input_ids,
                attention_mask=attention_mask,
                do_sample=True,
                temperature=temperature,
                top_p=top_p,
                repetition_penalty=repetition_penalty,
                max_new_tokens=max_new_tokens,
                num_return_sequences=1,
                eos_token_id=128258,
            )
        
        progress(0.6, "Processing speech tokens...")
        code_list = parse_output(generated_ids)
        
        progress(0.8, "Converting to audio...")
        audio_samples = redistribute_codes(code_list, snac_model)
        
        return (24000, audio_samples)  # Return sample rate and audio
    except Exception as e:
        print(f"Error generating speech: {e}")
        return None

# Create Gradio interface
with gr.Blocks(title="AI Podcaster") as demo:
    with gr.Row():
        with gr.Column(scale=1):
            gemini_api_key = gr.Textbox(label="Gemini API Key", type="password")
            content = gr.Textbox(label="Content", lines=5)
            uploaded_file = gr.File(label="Upload File")
            duration = gr.Slider(minimum=1, maximum=60, value=5, step=1, label="Duration (minutes)")
            num_hosts = gr.Radio(["1", "2"], label="Number of Hosts", value="1")
            generate_script_btn = gr.Button("Generate Podcast Script")

        with gr.Column(scale=2):
            script_output = gr.Textbox(label="Generated Script", lines=10)
            text_input = gr.Textbox(label="Text to speak", lines=5)
            voice = gr.Dropdown(choices=["Narrator", "Male", "Female"], value="Narrator", label="Voice")
            
            with gr.Row():
                temperature = gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature")
                top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.1, label="Top P")
            
            with gr.Row():
                repetition_penalty = gr.Slider(minimum=1.0, maximum=2.0, value=1.2, step=0.1, label="Repetition Penalty")
                max_new_tokens = gr.Slider(minimum=100, maximum=1000, value=500, step=50, label="Max New Tokens")
            
            submit_btn = gr.Button("Generate Speech")
            clear_btn = gr.Button("Clear")
                
        with gr.Column(scale=2):
            audio_output = gr.Audio(label="Generated Speech", type="numpy")
            
    # Set up event handlers
    generate_script_btn.click(
        fn=generate_podcast_script,
        inputs=[gemini_api_key, content, uploaded_file, duration, num_hosts],
        outputs=script_output
    )
    
    submit_btn.click(
        fn=generate_speech,
        inputs=[text_input, voice, temperature, top_p, repetition_penalty, max_new_tokens],
        outputs=audio_output
    )
    
    clear_btn.click(
        fn=lambda: (None, None),
        inputs=[],
        outputs=[text_input, audio_output]
    )

# Launch the app
if __name__ == "__main__":
    demo.queue().launch(share=False, ssr_mode=False)