Update README.md

README.md

@@ -1,14 +1,25 @@
 ---
-title: Qwen2.5 1.5B Instruct Gkd Demo   
-emoji: 📊   
-colorFrom: yellow   
-colorTo: red   
-sdk: gradio   
-sdk_version: 5.34.2   
-app_file: app.py   
-pinned: false   
-license: mit   
-short_description: Qwen2.5-1.5B-Instruct-gkd-demo   
+title: Qwen2.5 1.5B Instruct Gkd Demo
+emoji: 📊
+colorFrom: yellow
+colorTo: red
+sdk: gradio
+sdk_version: 5.34.2
+app_file: app.py
+pinned: false
+license: mit
+short_description: Qwen2.5-1.5B-Instruct-gkd-demo
+---
+
+# Qwen2.5 1.5B Instruct Gkd Demo
+
+A voice-enabled chatbot powered by:
+- `Qwen2.5-1.5B-Instruct-gkd` for language generation
+- `SpeechT5` for text-to-speech
+- `Whisper-small` for speech-to-text
+
+Try chatting, listen to the audio response, or upload an audio file for transcription.
+
 ---
 
 ## ✅ What’s Fixed & Improved
@@ -22,243 +33,4 @@ short_description: Qwen2.5-1.5B-Instruct-gkd-demo
 | ⚙️ **Model Loading Optimization** | Moved model loading into the first request (Hugging Face Spaces friendly) |
 | 🧼 **Code Cleanliness** | Better structure, comments, and readability |
 
----
-
-## 📦 Final Version of `app.py`
-
-Here is your updated file:
-
-```python
-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
-
-# --- 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
-
-# --- Helper: Split Text Into Chunks ---
-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()]
-
-# --- Load Models Function ---
-@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}")
-
-# --- Generate Response and Audio ---
-@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
-
-# --- Transcribe Audio ---
-@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])
-
-demo.queue().launch()
-```
-
----
-
-## ✅ Instructions for Uploading to Hugging Face Spaces
-
-1. **Go to your Space**: https://huggingface.co/spaces/ajsbsd/Qwen2.5-1.5B-Instruct-gkd-demo
-2. **Pause the CI**: Go to `Settings > Runtime`, and switch from "Always On" to "Manual"
-3. **Delete old app.py**
-4. **Upload this new file** as `app.py`
-5. **Start the CI again**
-
----
-
-## 🧩 Optional Enhancements
-
-Would you like me to help you with any of the following?
-
-- Add **status indicators** during model loading or generation
-- Allow **microphone input** directly in chat tab
-- Use `gr.State()` to store chat history more efficiently
-- Package models into a custom repo for faster load times
-
-Just let me know what you'd like next!Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---