Spaces:
Runtime error
Runtime error
| 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() |