app.py

import gradio as gr
import torch
import time
import librosa
import soundfile
import nemo.collections.asr as nemo_asr
import tempfile
import os
import uuid

from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
import torch

# PersistDataset -----
import os
import csv
import gradio as gr
from gradio import inputs, outputs
import huggingface_hub
from huggingface_hub import Repository, hf_hub_download, upload_file
from datetime import datetime
DATASET_REPO_URL = "https://huggingface.co/datasets/awacke1/Carddata.csv"
DATASET_REPO_ID = "awacke1/Carddata.csv"
DATA_FILENAME = "Carddata.csv"
DATA_FILE = os.path.join("data", DATA_FILENAME)
HF_TOKEN = os.environ.get("HF_TOKEN")

SCRIPT = """
<script>
if (!window.hasBeenRun) {
    window.hasBeenRun = true;
    console.log("should only happen once");
    document.querySelector("button.submit").click();
}
</script>
"""


try:
    hf_hub_download(
        repo_id=DATASET_REPO_ID,
        filename=DATA_FILENAME,
        cache_dir=DATA_DIRNAME,
        force_filename=DATA_FILENAME
    )
except:
    print("file not found")
repo = Repository(
    local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
)

def generate_html() -> str:
    with open(DATA_FILE) as csvfile:
        reader = csv.DictReader(csvfile)
        rows = []
        for row in reader:
            rows.append(row)
        rows.reverse()
        if len(rows) == 0:
            return "no messages yet"
        else:
            html = "<div class='chatbot'>"
            for row in rows:
                html += "<div>"
                html += f"<span>{row['inputs']}</span>"
                html += f"<span class='outputs'>{row['outputs']}</span>"
                html += "</div>"
            html += "</div>"
            return html
            
            
def store_message(name: str, message: str):
    if name and message:
        with open(DATA_FILE, "a") as csvfile:
            writer = csv.DictWriter(csvfile, fieldnames=["name", "message", "time"])
            writer.writerow(
                {"name": name.strip(), "message": message.strip(), "time": str(datetime.now())}
            )
        commit_url = repo.push_to_hub()
    return ""            


iface = gr.Interface(
    store_message,
    [
        inputs.Textbox(placeholder="Your name"),
        inputs.Textbox(placeholder="Your message", lines=2),
    ],
    "html",
    css="""
    .message {background-color:cornflowerblue;color:white; padding:4px;margin:4px;border-radius:4px; }
    """,
    title="Reading/writing to a HuggingFace dataset repo from Spaces",
    description=f"This is a demo of how to do simple *shared data persistence* in a Gradio Space, backed by a dataset repo.",
    article=f"The dataset repo is [{DATASET_REPO_URL}]({DATASET_REPO_URL})",
)


mname = "facebook/blenderbot-400M-distill"
model = BlenderbotForConditionalGeneration.from_pretrained(mname)
tokenizer = BlenderbotTokenizer.from_pretrained(mname)

def take_last_tokens(inputs, note_history, history):
    """Filter the last 128 tokens"""
    if inputs['input_ids'].shape[1] > 128:
        inputs['input_ids'] = torch.tensor([inputs['input_ids'][0][-128:].tolist()])
        inputs['attention_mask'] = torch.tensor([inputs['attention_mask'][0][-128:].tolist()])
        note_history = ['</s> <s>'.join(note_history[0].split('</s> <s>')[2:])]
        history = history[1:]
    return inputs, note_history, history

def add_note_to_history(note, note_history):
    """Add a note to the historical information"""
    note_history.append(note)
    note_history = '</s> <s>'.join(note_history)
    return [note_history]


def chat(message, history):
    history = history or []
    if history: 
        history_useful = ['</s> <s>'.join([str(a[0])+'</s> <s>'+str(a[1]) for a in history])]
    else:
        history_useful = []
    history_useful = add_note_to_history(message, history_useful)
    inputs = tokenizer(history_useful, return_tensors="pt")
    inputs, history_useful, history = take_last_tokens(inputs, history_useful, history)
    reply_ids = model.generate(**inputs)
    response = tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0]
    history_useful = add_note_to_history(response, history_useful)
    list_history = history_useful[0].split('</s> <s>')
    history.append((list_history[-2], list_history[-1]))
    store_message(message, response) # Save to dataset
    return history, history

SAMPLE_RATE = 16000
model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained("nvidia/stt_en_conformer_transducer_xlarge")
model.change_decoding_strategy(None)
model.eval()

def process_audio_file(file):
    data, sr = librosa.load(file)
    if sr != SAMPLE_RATE:
        data = librosa.resample(data, orig_sr=sr, target_sr=SAMPLE_RATE)
    # monochannel
    data = librosa.to_mono(data)
    return data

def transcribe(audio, state=""):
    # Grant additional context
    # time.sleep(1)
    if state is None:
        state = ""
    audio_data = process_audio_file(audio)
    with tempfile.TemporaryDirectory() as tmpdir:
        # Filepath transcribe
        audio_path = os.path.join(tmpdir, f'audio_{uuid.uuid4()}.wav')
        soundfile.write(audio_path, audio_data, SAMPLE_RATE)
        transcriptions = model.transcribe([audio_path])
       # Direct transcribe
        # transcriptions = model.transcribe([audio])
        # if transcriptions form a tuple (from RNNT), extract just "best" hypothesis
        if type(transcriptions) == tuple and len(transcriptions) == 2:
            transcriptions = transcriptions[0]
        transcriptions = transcriptions[0]
    state = state + transcriptions + " "
    store_message(state, state) # Save to dataset
    return state, state

iface = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.Audio(source="microphone", type='filepath', streaming=True),
        "state",
    ],
    outputs=[
        "textbox",
        "state",
    ],
    layout="horizontal",
    theme="huggingface",
    title="ASR Streaming Conformer Transducer Large - English",
    description="Demo for English speech recognition using Conformer Transducers",
    allow_flagging='never',
    live=True,    
    article=f"The dataset repo is [{DATASET_REPO_URL}]({DATASET_REPO_URL})"
)
iface.launch()