Delete app.py

app.py

@@ -1,366 +0,0 @@
-import gradio as gr
-import torch
-from transformers import (
-    AutoTokenizer,
-    AutoModelForCausalLM,
-    SpeechT5Processor,
-    SpeechT5ForTextToSpeech,
-    SpeechT5HifiGan,
-    WhisperProcessor, # For Speech-to-Text
-    WhisperForConditionalGeneration # For Speech-to-Text
-)
-from datasets import load_dataset # To get a speaker embedding for TTS
-import os
-import spaces # Import the spaces library for GPU decorator
-import tempfile # For creating temporary audio files
-import soundfile as sf # To save audio files
-import librosa # For loading audio files for transcription
-
-# --- Configuration for Language Model (LLM) ---
-HUGGINGFACE_MODEL_ID = "HuggingFaceH4/Qwen2.5-1.5B-Instruct-gkd"
-TORCH_DTYPE = torch.bfloat16
-MAX_NEW_TOKENS = 512
-DO_SAMPLE = True
-TEMPERATURE = 0.7
-TOP_K = 50
-TOP_P = 0.95
-
-# --- Configuration for Text-to-Speech (TTS) ---
-TTS_MODEL_ID = "microsoft/speecht5_tts"
-TTS_VOCODER_ID = "microsoft/speecht5_hifigan"
-
-# --- Configuration for Speech-to-Text (STT) ---
-STT_MODEL_ID = "openai/whisper-small" # Changed from 'openai/whisper-tiny' for better long audio transcription
-
-# --- Global variables for models and tokenizers/processors ---
-tokenizer = None
-llm_model = None
-tts_processor = None
-tts_model = None
-tts_vocoder = None
-speaker_embeddings = None
-whisper_processor = None
-whisper_model = None
-
-# --- Load All Models Function ---
-@spaces.GPU # Decorate with @spaces.GPU to signal this function needs GPU access
-def load_models():
-    """
-    Loads the language model, tokenizer, TTS models, speaker embeddings,
-    and STT (Whisper) models from Hugging Face Hub.
-    This function will be called once when the Gradio app starts up.
-    """
-    global tokenizer, llm_model, tts_processor, tts_model, tts_vocoder, speaker_embeddings
-    global whisper_processor, whisper_model
-
-    if (tokenizer is not None and llm_model is not None and tts_model is not None and
-        whisper_processor is not None and whisper_model is not None):
-        print("All models and tokenizers/processors already loaded.")
-        return
-
-    hf_token = os.environ.get("HF_TOKEN")
-
-    # Load Language Model (LLM)
-    print(f"Loading LLM tokenizer from: {HUGGINGFACE_MODEL_ID}")
-    try:
-        tokenizer = AutoTokenizer.from_pretrained(HUGGINGFACE_MODEL_ID, token=hf_token)
-        if tokenizer.pad_token is None:
-            tokenizer.pad_token = tokenizer.eos_token
-            print(f"Set tokenizer.pad_token to tokenizer.eos_token ({tokenizer.pad_token_id})")
-
-        print(f"Loading LLM model from: {HUGGINGFACE_MODEL_ID}...")
-        llm_model = AutoModelForCausalLM.from_pretrained(
-            HUGGINGFACE_MODEL_ID,
-            torch_dtype=TORCH_DTYPE,
-            device_map="auto",
-            token=hf_token
-        )
-        llm_model.eval()
-        print("LLM model loaded successfully.")
-    except Exception as e:
-        print(f"Error loading LLM model or tokenizer: {e}")
-        raise RuntimeError("Failed to load LLM model. Check your model ID/path and internet connection.")
-
-    # Load TTS models
-    print(f"Loading TTS processor, model, and vocoder from: {TTS_MODEL_ID}, {TTS_VOCODER_ID}")
-    try:
-        tts_processor = SpeechT5Processor.from_pretrained(TTS_MODEL_ID, token=hf_token)
-        tts_model = SpeechT5ForTextToSpeech.from_pretrained(TTS_MODEL_ID, token=hf_token)
-        tts_vocoder = SpeechT5HifiGan.from_pretrained(TTS_VOCODER_ID, token=hf_token)
-
-        print("Loading speaker embeddings for TTS...")
-        embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation", token=hf_token)
-        speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
-
-        device = llm_model.device if llm_model else 'cpu'
-        tts_model.to(device)
-        tts_vocoder.to(device)
-        speaker_embeddings = speaker_embeddings.to(device)
-        print(f"TTS models and speaker embeddings loaded successfully to device: {device}.")
-
-    except Exception as e:
-        print(f"Error loading TTS models or speaker embeddings: {e}")
-        tts_processor = None
-        tts_model = None
-        tts_vocoder = None
-        speaker_embeddings = None
-        raise RuntimeError("Failed to load TTS components. Check model IDs and internet connection.")
-
-    # Load STT (Whisper) model
-    print(f"Loading STT (Whisper) processor and model from: {STT_MODEL_ID}")
-    try:
-        whisper_processor = WhisperProcessor.from_pretrained(STT_MODEL_ID, token=hf_token)
-        whisper_model = WhisperForConditionalGeneration.from_pretrained(STT_MODEL_ID, token=hf_token)
-
-        device = llm_model.device if llm_model else 'cpu' # Use the same device as LLM
-        whisper_model.to(device)
-        print(f"STT (Whisper) model loaded successfully to device: {device}.")
-    except Exception as e:
-        print(f"Error loading STT (Whisper) model or processor: {e}")
-        whisper_processor = None
-        whisper_model = None
-        raise RuntimeError("Failed to load STT (Whisper) components. Check model ID and internet connection.")
-
-
-# --- Generate Response and Audio Function ---
-@spaces.GPU
-def generate_response_and_audio(message: str, history: list) -> tuple:
-    global tokenizer, llm_model, tts_processor, tts_model, tts_vocoder, speaker_embeddings
-
-    if tokenizer is None or llm_model is None or tts_model is None:
-        load_models()
-
-    if tokenizer is None or llm_model is None:
-        history.append({"role": "user", "content": message})
-        history.append({"role": "assistant", "content": "Error: Chatbot LLM not loaded. Please check logs."})
-        return history, None
-
-    # Initialize generated_text early
-    generated_text = ""
-
-    # --- 1. Generate Text Response (LLM) ---
-    messages = history.copy()
-    messages.append({"role": "user", "content": message})
-
-    try:
-        input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-    except Exception as e:
-        print(f"Error applying chat template: {e}")
-        input_text = ""
-        for item in history:
-            input_text += f"{item['role'].capitalize()}: {item['content']}\n"
-        input_text += f"User: {message}\nAssistant:"
-
-    try:
-        input_ids = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True).to(llm_model.device)
-        with torch.no_grad():
-            output_ids = llm_model.generate(
-                input_ids["input_ids"],
-                attention_mask=input_ids["attention_mask"],
-                max_new_tokens=MAX_NEW_TOKENS,
-                do_sample=DO_SAMPLE,
-                temperature=TEMPERATURE,
-                top_k=TOP_K,
-                top_p=TOP_P,
-                pad_token_id=tokenizer.eos_token_id
-            )
-
-        generated_token_ids = output_ids[0][input_ids["input_ids"].shape[-1]:]
-        generated_text = tokenizer.decode(generated_token_ids, skip_special_tokens=True).strip()
-
-    except Exception as e:
-        print(f"Error during LLM generation: {e}")
-        history.append({"role": "assistant", "content": "I encountered an error while generating a response."})
-        return history, None
-
-    # --- 2. Generate Audio from Response (TTS) ---
-    audio_path = None
-    if all([tts_processor, tts_model, tts_vocoder, speaker_embeddings]):
-        try:
-            device = llm_model.device if llm_model else 'cpu'
-            tts_model.to(device)
-            tts_vocoder.to(device)
-            speaker_embeddings = speaker_embeddings.to(device)
-
-            tts_inputs = tts_processor(
-                text=generated_text,
-                return_tensors="pt",
-                max_length=550,
-                truncation=True
-            ).to(device)
-
-            with torch.no_grad():
-                speech = tts_model.generate_speech(tts_inputs["input_ids"], speaker_embeddings, vocoder=tts_vocoder)
-
-            with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp_file:
-                audio_path = tmp_file.name
-                sf.write(audio_path, speech.cpu().numpy(), samplerate=16000)
-
-            print(f"Audio saved to: {audio_path}")
-
-        except Exception as e:
-            print(f"Error generating audio: {e}")
-            audio_path = None
-    else:
-        print("TTS components not fully loaded. Skipping audio generation.")
-
-    # --- 3. Update Chat History ---
-    history.append({"role": "assistant", "content": generated_text})
-    return history, audio_path
-    # --- 2. Generate Audio from Response (TTS) ---
-    audio_path = None
-    if tts_processor and tts_model and tts_vocoder and speaker_embeddings is not None:
-        try:
-            device = llm_model.device if llm_model else 'cpu'
-            tts_model.to(device)
-            tts_vocoder.to(device)
-            speaker_embeddings = speaker_embeddings.to(device)
-
-            tts_inputs = tts_processor(
-                text=generated_text,
-                return_tensors="pt",
-                max_length=550,
-                truncation=True
-            ).to(device)
-
-            with torch.no_grad():
-                speech = tts_model.generate_speech(tts_inputs["input_ids"], speaker_embeddings, vocoder=tts_vocoder)
-
-            with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp_file:
-                audio_path = tmp_file.name
-                sf.write(audio_path, speech.cpu().numpy(), samplerate=16000)
-            print(f"Audio saved to: {audio_path}")
-
-        except Exception as e:
-            print(f"Error generating audio: {e}")
-            audio_path = None
-    else:
-        print("TTS components not loaded. Skipping audio generation.")
-
-    # --- 3. Update Chat History ---
-    history.append({"role": "assistant", "content": generated_text})
-
-    return history, audio_path
-
-
-# --- Transcribe Audio Function (NEW) ---
-@spaces.GPU # This function also needs GPU access for Whisper inference
-def transcribe_audio(audio_filepath):
-    """
-    Transcribes an audio file using the loaded Whisper model.
-    Handles audio files of varying lengths.
-    """
-    global whisper_processor, whisper_model
-
-    if whisper_processor is None or whisper_model is None:
-        load_models() # Attempt to load if not already loaded
-
-    if whisper_processor is None or whisper_model is None:
-        return "Error: Speech-to-Text model not loaded. Please check logs."
-
-    if audio_filepath is None:
-        return "No audio input provided for transcription."
-
-    print(f"Transcribing audio from: {audio_filepath}")
-    try:
-        # Load audio file and resample to 16kHz (Whisper's required sample rate)
-        audio, sample_rate = librosa.load(audio_filepath, sr=16000)
-
-        # Process audio input for the Whisper model
-        # The Whisper `generate` method, especially with larger models, is designed
-        # to handle variable-length inputs by internally managing context.
-        input_features = whisper_processor(
-            audio,
-            sampling_rate=sample_rate,
-            return_tensors="pt"
-        ).input_features.to(whisper_model.device)
-
-        # Generate transcription IDs
-        predicted_ids = whisper_model.generate(input_features)
-
-        # Decode the IDs to text
-        transcription = whisper_processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
-        print(f"Transcription: {transcription}")
-        return transcription
-
-    except Exception as e:
-        print(f"Error during transcription: {e}")
-        return f"Transcription failed: {e}"
-
-
-# --- Gradio Interface ---
-with gr.Blocks() as demo:
-    gr.Markdown(
-        """
-        # HuggingFaceH4/Qwen2.5-1.5B-Instruct-gkd chat bot with Voice Input & Output
-        Type your message or speak into the microphone to chat with the model.
-        The chatbot's response will be spoken, and your audio input can be transcribed!
-        """
-    )
-
-    with gr.Tab("Chat with Voice"):
-        chatbot = gr.Chatbot(label="Conversation", type='messages')
-        with gr.Row():
-            text_input = gr.Textbox(
-                label="Your message",
-                placeholder="Type your message here...",
-                scale=4
-            )
-            submit_button = gr.Button("Send", scale=1)
-
-        audio_output = gr.Audio(
-            label="Listen to Response",
-            autoplay=True,
-            interactive=False
-        )
-
-        submit_button.click(
-            fn=generate_response_and_audio,
-            inputs=[text_input, chatbot],
-            outputs=[chatbot, audio_output],
-            queue=True
-        )
-        text_input.submit(
-            fn=generate_response_and_audio,
-            inputs=[text_input, chatbot],
-            outputs=[chatbot, audio_output],
-            queue=True
-        )
-
-    with gr.Tab("Audio Transcription"):
-        stt_audio_input = gr.Audio(
-            type="filepath",
-            label="Upload Audio or Record from Microphone",
-            # Removed 'microphone=True' and 'source' as they cause TypeError with older Gradio versions
-            # If you are still seeing TypeError for 'microphone', your Gradio version might be very old.
-            # In that case, this component will only support file uploads.
-            format="wav" # Ensure consistent format
-        )
-        transcribe_button = gr.Button("Transcribe Audio")
-        transcribed_text_output = gr.Textbox(
-            label="Transcription",
-            placeholder="Transcription will appear here...",
-            interactive=False
-        )
-        transcribe_button.click(
-            fn=transcribe_audio,
-            inputs=[stt_audio_input],
-            outputs=[transcribed_text_output],
-            queue=True
-        )
-
-    # Clear button for the entire interface
-    def clear_all():
-        return [], "", None, None, "" # Clear chatbot, text_input, audio_output, stt_audio_input, transcribed_text_output
-    clear_button = gr.Button("Clear All")
-    clear_button.click(
-        clear_all,
-        inputs=None,
-        outputs=[chatbot, text_input, audio_output, stt_audio_input, transcribed_text_output]
-    )
-
-# Load all models when the app starts up
-load_models()
-
-# Launch the Gradio app
-demo.queue().launch()