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from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
import torch
import gradio as gr
# 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")
# overriding/appending to the gradio template
SCRIPT = """
<script>
if (!window.hasBeenRun) {
window.hasBeenRun = true;
console.log("should only happen once");
document.querySelector("button.submit").click();
}
</script>
"""
#with open(os.path.join(gr.networking.STATIC_TEMPLATE_LIB, "frontend", "index.html"), "a") as f:
# f.write(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['name']}</span>"
html += f"<span class='message'>{row['message']}</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, "message": message, "time": str(datetime.now())}
)
commit_url = repo.push_to_hub()
return generate_html()
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})",
)
#iface.launch()
# -------
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]
title = "Blenderbot Tokenizer with Conditional Generation State of the Art"
description = """Blenderbot"""
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]))
return history, history
gr.Interface(
fn=chat,
theme="huggingface",
css=".footer {display:none !important}",
inputs=["text", "state"],
outputs=["chatbot", "state"],
title=title,
description=description,
allow_flagging="never",
).launch() |