java script

app.py

@@ -1,6 +1,8 @@
 from fastapi import FastAPI
 from transformers import pipeline
- 
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse
+
 # Create a new FastAPI app instance
 app = FastAPI()
  
@@ -8,13 +10,19 @@ app = FastAPI()
 # This function will be able to generate text
 # given an input.
 pipe = pipeline("text2text-generation", model="google/flan-t5-small")
- 
+
+app.mount("/", StaticFiles(directory="static", html=True), name="static")
+
+@app.get("/")
+def index() -> FileResponse:
+    return FileResponse(path="/app/static/index.html", media_type="text/html")
+
 # Define a function to handle the GET request at `/generate`
 # The generate() function is defined as a FastAPI route that takes a 
 # string parameter called text. The function generates text based on the # input using the pipeline() object, and returns a JSON response 
 # containing the generated text under the key "output"
-@app.get("/")
-def generate(text: str):
+@app.get("/infer_t5")
+def t5(text: str):
     """
     Using the text2text-generation pipeline from `transformers`, generate text
     from the given input text. The model used is `google/flan-t5-small`, which

static/index.html

@@ -0,0 +1,24 @@
+<main>
+    <section id="text-gen">
+      <h2>Text generation using Flan T5</h2>
+      <p>
+        Model:
+        <a
+          href="https://huggingface.co/google/flan-t5-small"
+          rel="noreferrer"
+          target="_blank"
+          >google/flan-t5-small
+        </a>
+      </p>
+      <form class="text-gen-form">
+        <label for="text-gen-input">Text prompt</label>
+        <input
+          id="text-gen-input"
+          type="text"
+          value="German: There are many ducks"
+        />
+        <button id="text-gen-submit">Submit</button>
+        <p class="text-gen-output"></p>
+      </form>
+    </section>
+</main>

static/script.js

@@ -0,0 +1,17 @@
+const textGenForm = document.querySelector(".text-gen-form");
+
+const translateText = async (text) => {
+  const inferResponse = await fetch(`infer_t5?input=${text}`);
+  const inferJson = await inferResponse.json();
+
+  return inferJson.output;
+};
+
+textGenForm.addEventListener("submit", async (event) => {
+  event.preventDefault();
+
+  const textGenInput = document.getElementById("text-gen-input");
+  const textGenParagraph = document.querySelector(".text-gen-output");
+
+  textGenParagraph.textContent = await translateText(textGenInput.value);
+});

static/style.css

@@ -0,0 +1,45 @@
+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);
+}
+
+form {
+  width: 30rem;
+  margin: 0 auto;
+}
+
+input {
+  width: 100%;
+}
+
+button {
+  cursor: pointer;
+}
+
+.text-gen-output {
+  min-height: 1.2rem;
+  margin: 1rem;
+  border: 0.5px solid grey;
+}