app2.py

import os
import gradio as gr
import numpy as np
import spaces
import torch
import torchaudio
from generator import Segment, load_csm_1b
from huggingface_hub import hf_hub_download, login
from watermarking import watermark
import whisper
from transformers import AutoTokenizer, AutoModelForCausalLM
import logging
from transformers import GenerationConfig

# Configure logging
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')

# --- Authentication and Configuration ---  (Moved BEFORE model loading)
try:
    api_key = os.getenv("HF_TOKEN")
    if not api_key:
        raise ValueError("HF_TOKEN not found in environment variables.")
    login(token=api_key)

    CSM_1B_HF_WATERMARK = list(map(int, os.getenv("WATERMARK_KEY").split(" ")))
    if not CSM_1B_HF_WATERMARK:
        raise ValueError("WATERMARK_KEY not found or invalid in environment variables.")

    gpu_timeout = int(os.getenv("GPU_TIMEOUT", 120))
except (ValueError, TypeError) as e:
    logging.error(f"Configuration error: {e}")
    raise

SPACE_INTRO_TEXT = """
# Sesame CSM 1B - Conversational Demo

This demo allows you to have a conversation with Sesame CSM 1B, leveraging Whisper for speech-to-text and Gemma for generating responses. This is an experimental integration and may require significant resources.

*Disclaimer: This demo relies on several large models. Expect longer processing times, and potential resource limitations.*
"""

# --- Model Loading --- (Moved INSIDE infer function)

# --- Constants ---  (Constants can stay outside)
SPEAKER_ID = 0
MAX_CONTEXT_SEGMENTS = 1
MAX_GEMMA_LENGTH = 150

# --- Global Conversation History ---
conversation_history = []

# --- Helper Functions ---

def transcribe_audio(audio_path: str, whisper_model) -> str:  # Pass whisper_model
    try:
        audio = whisper.load_audio(audio_path)
        audio = whisper.pad_or_trim(audio)
        result = whisper_model.transcribe(audio)
        return result["text"]
    except Exception as e:
        logging.error(f"Whisper transcription error: {e}")
        return "Error: Could not transcribe audio."

def generate_response(text: str, model_gemma, tokenizer_gemma, device) -> str: # Pass model and tokenizer
    try:
        # Gemma 3 chat template format
        messages = [{"role": "user", "content": text}]
        input = tokenizer_gemma.apply_chat_template(messages, return_tensors="pt").to(device)
        generation_config = GenerationConfig(
            max_new_tokens=MAX_GEMMA_LENGTH,
            early_stopping=True,
        )

        generated_output = model_gemma.generate(input, generation_config=generation_config)
        return tokenizer_gemma.decode(generated_output[0], skip_special_tokens=True)
        
        #input_text = "Reapond to the users prompt: " + text
        #input = tokenizer_gemma(input_text, return_tensors="pt").to(device)
        #generated_output = model_gemma.generate(**input, max_length=MAX_GEMMA_LENGTH, early_stopping=True)
        #return tokenizer_gemma.decode(generated_output[0], skip_special_tokens=True)
    except Exception as e:
        logging.error(f"Gemma response generation error: {e}")
        return "I'm sorry, I encountered an error generating a response."

def load_audio(audio_path: str, generator) -> torch.Tensor: #Pass generator
    try:
        audio_tensor, sample_rate = torchaudio.load(audio_path)
        audio_tensor = audio_tensor.mean(dim=0)
        if sample_rate != generator.sample_rate:
            audio_tensor = torchaudio.functional.resample(audio_tensor, orig_freq=sample_rate, new_freq=generator.sample_rate)
        return audio_tensor
    except Exception as e:
        logging.error(f"Audio loading error: {e}")
        raise gr.Error("Could not load or process the audio file.") from e

def clear_history():
    global conversation_history
    conversation_history = []
    logging.info("Conversation history cleared.")
    return "Conversation history cleared."

# --- Main Inference Function ---

@spaces.GPU(duration=gpu_timeout)  # Decorator FIRST
def infer(user_audio) -> tuple[int, np.ndarray]:
    # --- CUDA Availability Check (INSIDE infer) ---
    if torch.cuda.is_available():
        print(f"CUDA is available! Device count: {torch.cuda.device_count()}")
        print(f"CUDA device name: {torch.cuda.get_device_name(0)}")
        print(f"CUDA version: {torch.version.cuda}")
        device = "cuda"
    else:
        print("CUDA is NOT available.  Using CPU.")  #  Use CPU, don't raise
        device = "cpu"

    try:
      # --- Model Loading (INSIDE infer, after device is set) ---
        model_path = hf_hub_download(repo_id="sesame/csm-1b", filename="ckpt.pt")
        generator = load_csm_1b(model_path, device)
        logging.info("Sesame CSM 1B loaded successfully.")

        whisper_model = whisper.load_model("small.en", device=device)
        logging.info("Whisper model loaded successfully.")

        tokenizer_gemma = AutoTokenizer.from_pretrained("google/gemma-3-1b-it")
        model_gemma = AutoModelForCausalLM.from_pretrained("google/gemma-3-1b-it").to(device)
        logging.info("Gemma 3 1B pt model loaded successfully.")

        if not user_audio:
            raise ValueError("No audio input received.")
        return _infer(user_audio, generator, whisper_model, tokenizer_gemma, model_gemma, device) #Pass all models
    except Exception as e:
        logging.exception(f"Inference error: {e}")
        raise gr.Error(f"An error occurred during processing: {e}")

def _infer(user_audio, generator, whisper_model, tokenizer_gemma, model_gemma, device) -> tuple[int, np.ndarray]:
    global conversation_history

    try:
        user_text = transcribe_audio(user_audio, whisper_model) # Pass whisper_model
        logging.info(f"User: {user_text}")

        ai_text = generate_response(user_text, model_gemma, tokenizer_gemma, device)  # Pass model and tokenizer
        logging.info(f"AI: {ai_text}")

        try:
            ai_audio = generator.generate(
                text=ai_text,
                speaker=SPEAKER_ID,
                context=conversation_history,
                max_audio_length_ms=10_000,
            )
            logging.info("Audio generated successfully.")
        except Exception as e:
             logging.error(f"Sesame response generation error: {e}")
             raise gr.Error(f"Sesame response generation error: {e}")


        user_segment = Segment(speaker = 1, text = user_text, audio = load_audio(user_audio, generator)) #Pass Generator
        ai_segment =  Segment(speaker = SPEAKER_ID, text = ai_text, audio = ai_audio)
        conversation_history.append(user_segment)
        conversation_history.append(ai_segment)

        if len(conversation_history) > MAX_CONTEXT_SEGMENTS:
            conversation_history.pop(0)

        audio_tensor, wm_sample_rate = watermark(
            generator._watermarker, ai_audio, generator.sample_rate, CSM_1B_HF_WATERMARK
        )
        audio_tensor = torchaudio.functional.resample(
            audio_tensor, orig_freq=wm_sample_rate, new_freq=generator.sample_rate
        )

        ai_audio_array = (audio_tensor * 32768).to(torch.int16).cpu().numpy()
        return generator.sample_rate, ai_audio_array

    except Exception as e:
        logging.exception(f"Error in _infer: {e}")
        raise gr.Error(f"An error occurred during processing: {e}")

# --- Gradio Interface ---

with gr.Blocks() as app:
    gr.Markdown(SPACE_INTRO_TEXT)
    audio_input = gr.Audio(label="Your Input", type="filepath")
    audio_output = gr.Audio(label="AI Response")
    clear_button = gr.Button("Clear Conversation History")
    status_display = gr.Textbox(label="Status", visible=False)

    btn = gr.Button("Generate Response")
    btn.click(infer, inputs=[audio_input], outputs=[audio_output])
    clear_button.click(clear_history, outputs=[status_display])

app.launch(ssr_mode=False, share=True)