Spaces:
Runtime error
Runtime error
File size: 4,765 Bytes
795b0f2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 |
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")
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]
title = "Chatbot State of the Art now with Memory Saved to Dataset"
description = """Chatbot With Memory"""
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
gr.Interface(
fn=chat,
theme="huggingface",
css=".footer {display:none !important}",
inputs=["text", "state"],
outputs=["chatbot", "state"],
title=title,
allow_flagging="never",
description=f"Gradio chatbot backed by memory in a dataset repository.",
article=f"The dataset repo is [{DATASET_REPO_URL}]({DATASET_REPO_URL})"
).launch() |