Spaces:
Sleeping
Sleeping
Ron Au
commited on
Commit
•
db6e2f8
1
Parent(s):
26d3451
Initial Commit
Browse files- README.md +8 -5
- app.py +60 -0
- modules/dataset.py +19 -0
- modules/inference.py +11 -0
- requirements.txt +6 -0
- static/index.js +126 -0
- static/style.css +79 -0
- templates/index.html +0 -0
README.md
CHANGED
@@ -1,13 +1,16 @@
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---
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-
title: Flask
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emoji:
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colorFrom:
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colorTo:
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sdk: gradio
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sdk_version: 2.9.1
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app_file: app.py
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license: mit
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
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---
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title: Flask + dev server
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emoji: ⚗️
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colorFrom: gray
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colorTo: gray
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sdk: gradio
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sdk_version: 2.9.1
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python_version: 3.10.4
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app_file: app.py
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models: [osanseviero/BigGAN-deep-128, t5-small]
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datasets: [emotion]
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license: mit
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pinned: false
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
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app.py
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import os
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import requests
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import json
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from io import BytesIO
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from flask import Flask, jsonify, render_template, request, send_file
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from modules.inference import infer_t5
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from modules.dataset import query_emotion
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# https://huggingface.co/settings/tokens
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# https://huggingface.co/spaces/{username}/{space}/settings
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API_TOKEN = os.getenv("BIG_GAN_TOKEN")
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app = Flask(__name__)
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@app.route("/")
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def index():
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return render_template("index.html")
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@app.route("/infer_biggan")
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def biggan():
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input = request.args.get("input")
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output = requests.request(
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"POST",
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"https://api-inference.huggingface.co/models/osanseviero/BigGAN-deep-128",
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headers={"Authorization": f"Bearer {API_TOKEN}"},
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data=json.dumps(input),
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)
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return send_file(BytesIO(output.content), mimetype="image/png")
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@app.route("/infer_t5")
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def t5():
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input = request.args.get("input")
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output = infer_t5(input)
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return jsonify({"output": output})
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@app.route("/query_emotion")
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def emotion():
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start = request.args.get("start")
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end = request.args.get("end")
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print(start)
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print(end)
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output = query_emotion(int(start), int(end))
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return jsonify({"output": output})
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if __name__ == "__main__":
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app.run(host="0.0.0.0", port=7860)
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modules/dataset.py
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from datasets import load_dataset
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dataset = load_dataset("emotion", split="train")
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emotions = dataset.info.features["label"].names
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def query_emotion(start, end):
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rows = dataset[start:end]
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texts, labels = [rows[k] for k in rows.keys()]
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observations = []
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for i, text in enumerate(texts):
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observations.append({
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"text": text,
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"emotion": emotions[labels[i]],
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})
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return observations
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modules/inference.py
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from transformers import T5Tokenizer, T5ForConditionalGeneration
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tokenizer = T5Tokenizer.from_pretrained("t5-small")
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model = T5ForConditionalGeneration.from_pretrained("t5-small")
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def infer_t5(input):
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input_ids = tokenizer(input, return_tensors="pt").input_ids
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outputs = model.generate(input_ids)
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return tokenizer.decode(outputs[0], skip_special_tokens=True)
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requirements.txt
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datasets==2.*
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flask==2.1.*
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requests==2.27.*
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sentencepiece==0.1.*
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torch==1.11.*
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transformers==4.*
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static/index.js
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if (document.location.search.includes('dark-theme=true')) {
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document.body.classList.add('dark-theme');
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}
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let cursor = 0;
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const RANGE = 5;
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const LIMIT = 16_000;
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const textToImage = async (text) => {
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const inferenceResponse = await fetch(`infer_biggan?input=${text}`);
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const inferenceBlob = await inferenceResponse.blob();
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return URL.createObjectURL(inferenceBlob);
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};
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const translateText = async (text) => {
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const inferResponse = await fetch(`infer_t5?input=${text}`);
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const inferJson = await inferResponse.json();
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return inferJson.output;
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};
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const queryDataset = async (start, end) => {
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const queryResponse = await fetch(`query_emotion?start=${start}&end=${end}`);
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const queryJson = await queryResponse.json();
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return queryJson.output;
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};
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const updateTable = async (cursor, range = RANGE) => {
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const table = document.querySelector('.dataset-output');
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const fragment = new DocumentFragment();
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const observations = await queryDataset(cursor, cursor + range);
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for (const observation of observations) {
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let row = document.createElement('tr');
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let text = document.createElement('td');
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let emotion = document.createElement('td');
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text.textContent = observation.text;
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emotion.textContent = observation.emotion;
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row.appendChild(text);
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row.appendChild(emotion);
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fragment.appendChild(row);
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}
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table.innerHTML = '';
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table.appendChild(fragment);
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table.insertAdjacentHTML(
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'afterbegin',
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`<thead>
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<tr>
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<td>text</td>
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<td>emotion</td>
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</tr>
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</thead>`
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);
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};
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const imageGenSelect = document.getElementById('image-gen-input');
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const imageGenImage = document.querySelector('.image-gen-output');
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const textGenForm = document.querySelector('.text-gen-form');
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const tableButtonPrev = document.querySelector('.table-previous');
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const tableButtonNext = document.querySelector('.table-next');
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imageGenSelect.addEventListener('change', async (event) => {
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const value = event.target.value;
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try {
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imageGenImage.src = await textToImage(value);
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imageGenImage.alt = value + ' generated from BigGAN AI model';
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} catch (err) {
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console.error(err);
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}
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});
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textGenForm.addEventListener('submit', async (event) => {
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event.preventDefault();
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const textGenInput = document.getElementById('text-gen-input');
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const textGenParagraph = document.querySelector('.text-gen-output');
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try {
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textGenParagraph.textContent = await translateText(textGenInput.value);
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} catch (err) {
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console.error(err);
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}
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});
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tableButtonPrev.addEventListener('click', () => {
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cursor = cursor > RANGE ? cursor - RANGE : 0;
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if (cursor < RANGE) {
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tableButtonPrev.classList.add('hidden');
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}
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if (cursor < LIMIT - RANGE) {
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tableButtonNext.classList.remove('hidden');
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}
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updateTable(cursor);
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});
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tableButtonNext.addEventListener('click', () => {
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cursor = cursor < LIMIT - RANGE ? cursor + RANGE : cursor;
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if (cursor >= RANGE) {
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tableButtonPrev.classList.remove('hidden');
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}
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if (cursor >= LIMIT - RANGE) {
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tableButtonNext.classList.add('hidden');
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}
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updateTable(cursor);
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});
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textToImage(imageGenSelect.value)
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.then((image) => (imageGenImage.src = image))
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.catch(console.error);
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updateTable(cursor)
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.catch(console.error);
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static/style.css
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body {
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--text: hsl(0 0% 15%);
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padding: 2.5rem;
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font-family: sans-serif;
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color: var(--text);
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}
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body.dark-theme {
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--text: hsl(0 0% 90%);
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background-color: hsl(223 39% 7%);
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}
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main {
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max-width: 80rem;
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text-align: center;
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}
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section {
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display: flex;
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flex-direction: column;
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align-items: center;
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}
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a {
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color: var(--text);
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}
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select, input, button, .text-gen-output {
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padding: 0.5rem 1rem;
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}
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select, img, input {
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margin: 0.5rem auto 1rem;
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}
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form {
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width: 25rem;
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margin: 0 auto;
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}
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input {
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width: 70%;
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}
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button {
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cursor: pointer;
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}
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.text-gen-output {
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min-height: 1.2rem;
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margin: 1rem;
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border: 0.5px solid grey;
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}
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#dataset button {
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width: 6rem;
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margin: 0.5rem;
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}
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#dataset button.hidden {
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visibility: hidden;
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}
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table {
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max-width: 40rem;
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text-align: left;
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border-collapse: collapse;
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}
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thead {
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font-weight: bold;
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}
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td {
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padding: 0.5rem;
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}
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td:not(thead td) {
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border: 0.5px solid grey;
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}
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templates/index.html
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