Delete app.py

app.py

@@ -1,517 +0,0 @@
-import gradio as gr
-import torch
-from transformers import (
-    AutoTokenizer, AutoModelForCausalLM,
-    SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan,
-    WhisperProcessor, WhisperForConditionalGeneration
-)
-from datasets import load_dataset
-import os
-import spaces
-import tempfile
-import soundfile as sf
-import librosa
-import yaml
-
-# ================== Configuration ==================
-HUGGINGFACE_MODEL_ID = "HuggingFaceH4/Qwen2.5-1.5B-Instruct-gkd"
-TORCH_DTYPE = torch.bfloat16
-MAX_NEW_TOKENS = 512
-DO_SAMPLE = Trueimport gradio as gr
-import torch
-from transformers import (
-    AutoTokenizer, AutoModelForCausalLM,
-    SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan,
-    WhisperProcessor, WhisperForConditionalGeneration
-)
-from datasets import load_dataset
-import os
-import spaces
-import tempfile
-import soundfile as sf
-import librosa
-import yaml
-
-# ================== Configuration ==================
-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
-
-TTS_MODEL_ID = "microsoft/speecht5_tts"
-TTS_VOCODER_ID = "microsoft/speecht5_hifigan"
-STT_MODEL_ID = "openai/whisper-small"
-
-# ================== Global Variables ==================
-tokenizer = None
-llm_model = None
-tts_processor = None
-tts_model = None
-tts_vocoder = None
-speaker_embeddings = None
-whisper_processor = None
-whisper_model = None
-first_load = True
-
-# ================== UI Helpers ==================
-def generate_pretty_html(data):
-    html = """
-    <div style="font-family: Arial, sans-serif; max-width: 600px; margin: auto; 
-                background-color: #f9f9f9; border-radius: 10px; padding: 20px;
-                box-shadow: 0 4px 12px rgba(0,0,0,0.1);">
-      <h2 style="color: #2c3e50; border-bottom: 2px solid #ddd; padding-bottom: 10px;">Model Info</h2>
-    """
-    for key, value in data.items():
-        html += f"""
-        <div style="margin-bottom: 12px;">
-          <strong style="color: #34495e; display: inline-block; width: 160px;">{key}:</strong>
-          <span style="color: #2c3e50;">{value}</span>
-        </div>
-        """
-    html += "</div>"
-    return html
-
-def load_config():
-    with open("config.yaml", "r", encoding="utf-8") as f:
-        return yaml.safe_load(f)  # Loads only the first document
-
-def render_modern_info():
-    try:
-        config = load_config()
-        return generate_pretty_html(config)
-    except Exception as e:
-        return f"<div style='color: red;'>Error loading config: {str(e)}</div>"
-
-def load_readme():
-    with open("README.md", "r", encoding="utf-8") as f:
-        return f.read()
-
-# ================== Helper Functions ==================
-def split_text_into_chunks(text, max_chars=400):
-    sentences = text.replace("...", ".").split(". ")
-    chunks = []
-    current_chunk = ""
-    for sentence in sentences:
-        if len(current_chunk) + len(sentence) + 2 < max_chars:
-            current_chunk += ". " + sentence if current_chunk else sentence
-        else:
-            chunks.append(current_chunk)
-            current_chunk = sentence
-    if current_chunk:
-        chunks.append(current_chunk)
-    return [f"{chunk}." for chunk in chunks if chunk.strip()]
-
-# ================== Model Loading ==================
-@spaces.GPU
-def load_models():
-    global tokenizer, llm_model, tts_processor, tts_model, tts_vocoder, speaker_embeddings, whisper_processor, whisper_model
-    hf_token = os.environ.get("HF_TOKEN")
-
-    # LLM
-    if tokenizer is None or llm_model is None:
-        try:
-            tokenizer = AutoTokenizer.from_pretrained(HUGGINGFACE_MODEL_ID, token=hf_token)
-            if tokenizer.pad_token is None:
-                tokenizer.pad_token = tokenizer.eos_token
-            llm_model = AutoModelForCausalLM.from_pretrained(
-                HUGGINGFACE_MODEL_ID,
-                torch_dtype=TORCH_DTYPE,
-                device_map="auto",
-                token=hf_token
-            ).eval()
-            print("LLM loaded successfully.")
-        except Exception as e:
-            print(f"Error loading LLM: {e}")
-
-    # TTS
-    if tts_processor is None or tts_model is None or tts_vocoder is None:
-        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)
-            embeddings = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation", token=hf_token)
-            speaker_embeddings = torch.tensor(embeddings[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("TTS models loaded.")
-        except Exception as e:
-            print(f"Error loading TTS: {e}")
-
-    # STT
-    if whisper_processor is None or whisper_model is None:
-        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'
-            whisper_model.to(device)
-            print("Whisper loaded.")
-        except Exception as e:
-            print(f"Error loading Whisper: {e}")
-
-# ================== Chat & Audio Functions ==================
-@spaces.GPU
-def generate_response_and_audio(message, history):
-    global first_load
-    if first_load:
-        load_models()
-        first_load = False
-
-    global tokenizer, llm_model, tts_processor, tts_model, tts_vocoder, speaker_embeddings
-
-    if tokenizer is None or llm_model is None:
-        return [{"role": "assistant", "content": "Error: LLM not loaded."}], None
-
-    messages = history.copy()
-    messages.append({"role": "user", "content": message})
-
-    try:
-        input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-    except:
-        input_text = ""
-        for item in history:
-            input_text += f"{item['role'].capitalize()}: {item['content']}\n"
-        input_text += f"User: {message}\nAssistant:"
-
-    try:
-        inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True).to(llm_model.device)
-        output_ids = llm_model.generate(
-            inputs["input_ids"],
-            attention_mask=inputs["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_text = tokenizer.decode(output_ids[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True).strip()
-    except Exception as e:
-        print(f"LLM error: {e}")
-        return history + [{"role": "assistant", "content": "I had an issue generating a response."}], None
-
-    audio_path = None
-    if None not in [tts_processor, tts_model, tts_vocoder, speaker_embeddings]:
-        try:
-            device = llm_model.device
-            text_chunks = split_text_into_chunks(generated_text)
-
-            full_speech = []
-            for chunk in text_chunks:
-                tts_inputs = tts_processor(text=chunk, return_tensors="pt", max_length=512, truncation=True).to(device)
-                speech = tts_model.generate_speech(tts_inputs["input_ids"], speaker_embeddings, vocoder=tts_vocoder)
-                full_speech.append(speech.cpu())
-
-            full_speech_tensor = torch.cat(full_speech, dim=0)
-
-            with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp_file:
-                audio_path = tmp_file.name
-                sf.write(audio_path, full_speech_tensor.numpy(), samplerate=16000)
-
-        except Exception as e:
-            print(f"TTS error: {e}")
-
-    return history + [{"role": "assistant", "content": generated_text}], audio_path
-
-@spaces.GPU
-def transcribe_audio(filepath):
-    global first_load
-    if first_load:
-        load_models()
-        first_load = False
-
-    global whisper_processor, whisper_model
-    if whisper_model is None:
-        return "Whisper model not loaded."
-
-    try:
-        audio, sr = librosa.load(filepath, sr=16000)
-        inputs = whisper_processor(audio, sampling_rate=sr, return_tensors="pt").input_features.to(whisper_model.device)
-        outputs = whisper_model.generate(inputs)
-        return whisper_processor.batch_decode(outputs, skip_special_tokens=True)[0]
-    except Exception as e:
-        return f"Transcription failed: {e}"
-
-# ================== Gradio UI ==================
-with gr.Blocks() as demo:
-    gr.Markdown("# Qwen2.5 Chatbot with Voice Input/Output")
-    
-    with gr.Tab("Chat"):
-        chatbot = gr.Chatbot(type='messages')
-        text_input = gr.Textbox(placeholder="Type your message...")
-        audio_output = gr.Audio(label="Response Audio", autoplay=True)
-        text_input.submit(generate_response_and_audio, [text_input, chatbot], [chatbot, audio_output])
-
-    with gr.Tab("Transcribe"):
-        audio_input = gr.Audio(type="filepath", label="Upload Audio")
-        transcribed = gr.Textbox(label="Transcription")
-        audio_input.upload(transcribe_audio, audio_input, transcribed)
-
-    clear_btn = gr.Button("Clear All")
-    clear_btn.click(lambda: ([], "", None), None, [chatbot, text_input, audio_output])
-    
-    #gr.Markdown(load_readme())
-    #gr.Markdown("---")
-
-    # ✅ Define html_output BEFORE using it
-    html_output = gr.HTML("<div style='text-align:center; padding: 20px;'>Loading model info...</div>")
-    
-    # ✅ Now this works!
-    demo.load(fn=render_modern_info, outputs=html_output)
-
-# ================== Launch App ==================
-demo.queue().launch()
-TEMPERATURE = 0.7
-TOP_K = 50
-TOP_P = 0.95
-
-TTS_MODEL_ID = "microsoft/speecht5_tts"
-TTS_VOCODER_ID = "microsoft/speecht5_hifigan"
-STT_MODEL_ID = "openai/whisper-small"
-
-# ================== Global Variables ==================
-tokenizer = None
-llm_model = None
-tts_processor = None
-tts_model = None
-tts_vocoder = None
-speaker_embeddings = None
-whisper_processor = None
-whisper_model = None
-first_load = True
-
-# ================== UI Helpers ==================
-def generate_pretty_html(data):
-    html = """
-    <div style="font-family: Arial, sans-serif; max-width: 600px; margin: auto; 
-                background-color: #f9f9f9; border-radius: 10px; padding: 20px;
-                box-shadow: 0 4px 12px rgba(0,0,0,0.1);">
-      <h2 style="color: #2c3e50; border-bottom: 2px solid #ddd; padding-bottom: 10px;">Model Info</h2>
-    """
-    for key, value in data.items():
-        html += f"""
-        <div style="margin-bottom: 12px;">
-          <strong style="color: #34495e; display: inline-block; width: 160px;">{key}:</strong>
-          <span style="color: #2c3e50;">{value}</span>
-        </div>
-        """
-    html += "</div>"
-    return html
-
-def load_config():
-    with open("config.yaml", "r", encoding="utf-8") as f:
-        return yaml.safe_load(f)  # Loads only the first document
-
-def render_modern_info():
-    try:
-        config = load_config()
-        return generate_pretty_html(config)
-    except Exception as e:
-        return f"<div style='color: red;'>Error loading config: {str(e)}</div>"
-
-def load_readme():
-    with open("README.md", "r", encoding="utf-8") as f:
-        return f.read()
-
-# ================== Helper Functions ==================
-def split_text_into_chunks(text, max_chars=400):
-    sentences = text.replace("...", ".").split(". ")
-    chunks = []
-    current_chunk = ""
-    for sentence in sentences:
-        if len(current_chunk) + len(sentence) + 2 < max_chars:
-            current_chunk += ". " + sentence if current_chunk else sentence
-        else:
-            chunks.append(current_chunk)
-            current_chunk = sentence
-    if current_chunk:
-        chunks.append(current_chunk)
-    return [f"{chunk}." for chunk in chunks if chunk.strip()]
-
-# ================== Model Loading ==================
-@spaces.GPU
-def load_models():
-    global tokenizer, llm_model, tts_processor, tts_model, tts_vocoder, speaker_embeddings, whisper_processor, whisper_model
-    hf_token = os.environ.get("HF_TOKEN")
-
-    # LLM
-    if tokenizer is None or llm_model is None:
-        try:
-            tokenizer = AutoTokenizer.from_pretrained(HUGGINGFACE_MODEL_ID, token=hf_token)
-            if tokenizer.pad_token is None:
-                tokenizer.pad_token = tokenizer.eos_token
-            llm_model = AutoModelForCausalLM.from_pretrained(
-                HUGGINGFACE_MODEL_ID,
-                torch_dtype=TORCH_DTYPE,
-                device_map="auto",
-                token=hf_token
-            ).eval()
-            print("LLM loaded successfully.")
-        except Exception as e:
-            print(f"Error loading LLM: {e}")
-
-    # TTS
-    if tts_processor is None or tts_model is None or tts_vocoder is None:
-        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)
-            embeddings = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation", token=hf_token)
-            speaker_embeddings = torch.tensor(embeddings[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("TTS models loaded.")
-        except Exception as e:
-            print(f"Error loading TTS: {e}")
-
-    # STT
-    if whisper_processor is None or whisper_model is None:
-        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'
-            whisper_model.to(device)
-            print("Whisper loaded.")
-        except Exception as e:
-            print(f"Error loading Whisper: {e}")
-
-# ================== Chat & Audio Functions ==================
-@spaces.GPU
-def generate_response_and_audio(message, history):
-    global first_load
-    if first_load:
-        load_models()
-        first_load = False
-
-    global tokenizer, llm_model, tts_processor, tts_model, tts_vocoder, speaker_embeddings
-
-    if tokenizer is None or llm_model is None:
-        return [{"role": "assistant", "content": "Error: LLM not loaded."}], None
-
-    messages = history.copy()
-    messages.append({"role": "user", "content": message})
-
-    try:
-        input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-    except:
-        input_text = ""
-        for item in history:
-            input_text += f"{item['role'].capitalize()}: {item['content']}\n"
-        input_text += f"User: {message}\nAssistant:"
-
-    try:
-        inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True).to(llm_model.device)
-        output_ids = llm_model.generate(
-            inputs["input_ids"],
-            attention_mask=inputs["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_text = tokenizer.decode(output_ids[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True).strip()
-    except Exception as e:
-        print(f"LLM error: {e}")
-        return history + [{"role": "assistant", "content": "I had an issue generating a response."}], None
-
-    audio_path = None
-    if None not in [tts_processor, tts_model, tts_vocoder, speaker_embeddings]:
-        try:
-            device = llm_model.device
-            text_chunks = split_text_into_chunks(generated_text)
-
-            full_speech = []
-            for chunk in text_chunks:
-                tts_inputs = tts_processor(text=chunk, return_tensors="pt", max_length=512, truncation=True).to(device)
-                speech = tts_model.generate_speech(tts_inputs["input_ids"], speaker_embeddings, vocoder=tts_vocoder)
-                full_speech.append(speech.cpu())
-
-            full_speech_tensor = torch.cat(full_speech, dim=0)
-
-            with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp_file:
-                audio_path = tmp_file.name
-                sf.write(audio_path, full_speech_tensor.numpy(), samplerate=16000)
-
-        except Exception as e:
-            print(f"TTS error: {e}")
-
-    return history + [{"role": "assistant", "content": generated_text}], audio_path
-
-@spaces.GPU
-def transcribe_audio(filepath):
-    global first_load
-    if first_load:
-        load_models()
-        first_load = False
-
-    global whisper_processor, whisper_model
-    if whisper_model is None:
-        return "Whisper model not loaded."
-
-    try:
-        audio, sr = librosa.load(filepath, sr=16000)
-        inputs = whisper_processor(audio, sampling_rate=sr, return_tensors="pt").input_features.to(whisper_model.device)
-        outputs = whisper_model.generate(inputs)
-        return whisper_processor.batch_decode(outputs, skip_special_tokens=True)[0]
-    except Exception as e:
-        return f"Transcription failed: {e}"
-
-# ================== Gradio UI ==================
-with gr.Blocks(head="""
-    <script src="https://cdn.tailwindcss.com "></script>
-""") as demo:
-    gr.Markdown("""
-    <div class="bg-gray-900 text-white p-4 rounded-lg shadow-md mb-6">
-      <h1 class="text-2xl font-bold">Qwen2.5 Chatbot with Voice Input/Output</h1>
-      <p class="text-gray-300">Powered by Gradio + TailwindCSS</p>
-    </div>
-    """)
-
-    with gr.Tab("Chat"):
-        gr.HTML("""
-        <div class="bg-gray-800 p-4 rounded-lg mb-4">
-          <label class="block text-gray-300 font-medium mb-2">Chat Interface</label>
-        </div>
-        """)
-        chatbot = gr.Chatbot(type='messages', elem_classes=["bg-gray-800", "text-white"])
-        text_input = gr.Textbox(
-            placeholder="Type your message...",
-            label="User Input",
-            elem_classes=["bg-gray-700", "text-white", "border-gray-600"]
-        )
-        audio_output = gr.Audio(label="Response Audio", autoplay=True)
-        text_input.submit(generate_response_and_audio, [text_input, chatbot], [chatbot, audio_output])
-
-    with gr.Tab("Transcribe"):
-        gr.HTML("""
-        <div class="bg-gray-800 p-4 rounded-lg mb-4">
-          <label class="block text-gray-300 font-medium mb-2">Audio Transcription</label>
-        </div>
-        """)
-        audio_input = gr.Audio(type="filepath", label="Upload Audio")
-        transcribed = gr.Textbox(
-            label="Transcription",
-            elem_classes=["bg-gray-700", "text-white", "border-gray-600"]
-        )
-        audio_input.upload(transcribe_audio, audio_input, transcribed)
-
-    clear_btn = gr.Button("Clear All", elem_classes=["bg-gray-600", "hover:bg-gray-500", "text-white", "mt-4"])
-    clear_btn.click(lambda: ([], "", None), None, [chatbot, text_input, audio_output])
-
-    html_output = gr.HTML("""
-    <div class="bg-gray-800 text-white p-4 rounded-lg mt-6 text-center">
-      Loading model info...
-    </div>
-    """)
-    demo.load(fn=render_modern_info, outputs=html_output)
-
-# ================== Launch App ==================
-demo.queue().launch()