update_app

README.md

@@ -1,16 +0,0 @@
----
-title: Flask + dev server
-emoji: ⚗️
-colorFrom: gray
-colorTo: gray
-sdk: gradio
-sdk_version: 2.9.1
-python_version: 3.10.4
-app_file: app.py
-models: [osanseviero/BigGAN-deep-128, t5-small]
-datasets: [emotion]
-license: mit
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

app.py

@@ -1,57 +1,28 @@
-import os
-import requests
-import json
 from io import BytesIO
+from flask import Flask, jsonify, request
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
 
-from flask import Flask, jsonify, render_template, request, send_file
+# Load the fine-tuned model and tokenizer
+model_path = "gpt2"
+tokenizer = GPT2Tokenizer.from_pretrained(model_path)
+model = GPT2LMHeadModel.from_pretrained(model_path)
 
-from modules.inference import infer_t5
-from modules.dataset import query_emotion
 
-# https://huggingface.co/settings/tokens
-# https://huggingface.co/spaces/{username}/{space}/settings
-API_TOKEN = os.getenv("BIG_GAN_TOKEN")
+def infer_title(input):
+    input_text = "Q: " + input + " A:"
+    input_ids = tokenizer.encode(input_text, return_tensors='pt')
+    output = model.generate(input_ids, max_length=50, num_return_sequences=1)
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    return response
 
 app = Flask(__name__)
 
 
 @app.route("/")
-def index():
-    return render_template("index.html")
-
-
-@app.route("/infer_biggan")
-def biggan():
-    input = request.args.get("input")
-
-    output = requests.request(
-        "POST",
-        "https://api-inference.huggingface.co/models/osanseviero/BigGAN-deep-128",
-        headers={"Authorization": f"Bearer {API_TOKEN}"},
-        data=json.dumps(input),
-    )
-
-    return send_file(BytesIO(output.content), mimetype="image/png")
-
-
-@app.route("/infer_t5")
-def t5():
+def endpoint():
     input = request.args.get("input")
 
-    output = infer_t5(input)
-
-    return jsonify({"output": output})
-
-
-@app.route("/query_emotion")
-def emotion():
-    start = request.args.get("start")
-    end = request.args.get("end")
-
-    print(start)
-    print(end)
-
-    output = query_emotion(int(start), int(end))
+    output = infer_title(input)
 
     return jsonify({"output": output})
 

modules/dataset.py

@@ -1,16 +0,0 @@
-from datasets import load_dataset
-
-dataset = load_dataset("go_emotions", split="train")
-
-emotions = dataset.info.features["labels"].feature.names
-
-
-def query_emotion(start, end):
-    rows = dataset[start:end]
-
-    observations = [
-        {"text": r[0], "emotion": emotions[r[1][0]]}
-        for r in zip(rows["text"], rows["labels"])
-    ]
-
-    return observations

modules/inference.py

@@ -1,15 +0,0 @@
-from transformers import GPT2Tokenizer, GPT2LMHeadModel
-
-# Load the fine-tuned model and tokenizer
-model_path = "gpt2"
-tokenizer = GPT2Tokenizer.from_pretrained(model_path)
-model = GPT2LMHeadModel.from_pretrained(model_path)
-
-
-
-def infer_t5(input):
-    input_text = "Q: " + input + " A:"
-    input_ids = tokenizer.encode(input_text, return_tensors='pt')
-    output = model.generate(input_ids, max_length=50, temperature=0.7, num_return_sequences=1)
-    response = tokenizer.decode(output[0], skip_special_tokens=True)
-    return response

requirements.txt

@@ -1,4 +1,3 @@
-datasets==2.*
 flask==3.0.0
 requests==2.31.*
 sentencepiece==0.1.*

static/index.js

@@ -1,126 +0,0 @@
-if (document.location.search.includes('dark-theme=true')) {
-  document.body.classList.add('dark-theme');
-}
-
-let cursor = 0;
-const RANGE = 5;
-const LIMIT = 16_000;
-
-const textToImage = async (text) => {
-  const inferenceResponse = await fetch(`infer_biggan?input=${text}`);
-  const inferenceBlob = await inferenceResponse.blob();
-
-  return URL.createObjectURL(inferenceBlob);
-};
-
-const translateText = async (text) => {
-  const inferResponse = await fetch(`infer_t5?input=${text}`);
-  const inferJson = await inferResponse.json();
-
-  return inferJson.output;
-};
-
-const queryDataset = async (start, end) => {
-  const queryResponse = await fetch(`query_emotion?start=${start}&end=${end}`);
-  const queryJson = await queryResponse.json();
-
-  return queryJson.output;
-};
-
-const updateTable = async (cursor, range = RANGE) => {
-  const table = document.querySelector('.dataset-output');
-
-  const fragment = new DocumentFragment();
-
-  const observations = await queryDataset(cursor, cursor + range);
-
-  for (const observation of observations) {
-    let row = document.createElement('tr');
-    let text = document.createElement('td');
-    let emotion = document.createElement('td');
-
-    text.textContent = observation.text;
-    emotion.textContent = observation.emotion;
-
-    row.appendChild(text);
-    row.appendChild(emotion);
-    fragment.appendChild(row);
-  }
-
-  table.innerHTML = '';
-
-  table.appendChild(fragment);
-
-  table.insertAdjacentHTML(
-    'afterbegin',
-    `<thead>
-      <tr>
-        <td>text</td>
-        <td>emotion</td>
-      </tr>
-    </thead>`
-  );
-};
-
-const imageGenSelect = document.getElementById('image-gen-input');
-const imageGenImage = document.querySelector('.image-gen-output');
-const textGenForm = document.querySelector('.text-gen-form');
-const tableButtonPrev = document.querySelector('.table-previous');
-const tableButtonNext = document.querySelector('.table-next');
-
-imageGenSelect.addEventListener('change', async (event) => {
-  const value = event.target.value;
-
-  try {
-    imageGenImage.src = await textToImage(value);
-    imageGenImage.alt = value + ' generated from BigGAN AI model';
-  } catch (err) {
-    console.error(err);
-  }
-});
-
-textGenForm.addEventListener('submit', async (event) => {
-  event.preventDefault();
-
-  const textGenInput = document.getElementById('text-gen-input');
-  const textGenParagraph = document.querySelector('.text-gen-output');
-
-  try {
-    textGenParagraph.textContent = await translateText(textGenInput.value);
-  } catch (err) {
-    console.error(err);
-  }
-});
-
-tableButtonPrev.addEventListener('click', () => {
-  cursor = cursor > RANGE ? cursor - RANGE : 0;
-
-  if (cursor < RANGE) {
-    tableButtonPrev.classList.add('hidden');
-  }
-  if (cursor < LIMIT - RANGE) {
-    tableButtonNext.classList.remove('hidden');
-  }
-
-  updateTable(cursor);
-});
-
-tableButtonNext.addEventListener('click', () => {
-  cursor = cursor < LIMIT - RANGE ? cursor + RANGE : cursor;
-
-  if (cursor >= RANGE) {
-    tableButtonPrev.classList.remove('hidden');
-  }
-  if (cursor >= LIMIT - RANGE) {
-    tableButtonNext.classList.add('hidden');
-  }
-
-  updateTable(cursor);
-});
-
-textToImage(imageGenSelect.value)
-  .then((image) => (imageGenImage.src = image))
-  .catch(console.error);
-
-updateTable(cursor)
-  .catch(console.error);

static/style.css

@@ -1,79 +0,0 @@
-body {
-  --text: hsl(0 0% 15%);
-  padding: 2.5rem;
-  font-family: sans-serif;
-  color: var(--text);
-}
-body.dark-theme {
-  --text: hsl(0 0% 90%);
-  background-color: hsl(223 39% 7%);
-}
-
-main {
-  max-width: 80rem;
-  text-align: center;
-}
-
-section {
-  display: flex;
-  flex-direction: column;
-  align-items: center;
-}
-
-a {
-  color: var(--text);
-}
-
-select, input, button, .text-gen-output {
-  padding: 0.5rem 1rem;
-}
-
-select, img, input {
-  margin: 0.5rem auto 1rem;
-}
-
-form {
-  width: 25rem;
-  margin: 0 auto;
-}
-
-input {
-  width: 70%;
-}
-
-button {
-  cursor: pointer;
-}
-
-.text-gen-output {
-  min-height: 1.2rem;
-  margin: 1rem;
-  border: 0.5px solid grey;
-}
-
-#dataset button {
-  width: 6rem;
-  margin: 0.5rem;
-}
-
-#dataset button.hidden {
-  visibility: hidden;
-}
-
-table {
-  max-width: 40rem;
-  text-align: left;
-  border-collapse: collapse;
-}
-
-thead {
-  font-weight: bold;
-}
-
-td {
-  padding: 0.5rem;
-}
-
-td:not(thead td) {
-  border: 0.5px solid grey;
-}