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radames commited on Oct 6, 2023

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29710d4

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1 Parent(s): f4070ba

Update app.py

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app.py +40 -369

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@@ -1,374 +1,45 @@
-<html>
-  <head>
-    <meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
-    <title>Candle Bert</title>
-  </head>
-  <body></body>
-</html>
-<!DOCTYPE html>
-<html>
-  <head>
-    <meta charset="UTF-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <style>
-      @import url("https://fonts.googleapis.com/css2?family=Source+Code+Pro:wght@200;300;400&family=Source+Sans+3:wght@100;200;300;400;500;600;700;800;900&display=swap");
-      html,
-      body {
-        font-family: "Source Sans 3", sans-serif;
-      }
-    </style>
-    <script src="https://cdn.tailwindcss.com"></script>
-    <script type="module" src="./code.js"></script>
-    <script type="module">
-      import { hcl } from "https://cdn.skypack.dev/d3-color@3";
-      import { interpolateReds } from "https://cdn.skypack.dev/d3-scale-chromatic@3";
-      import { scaleLinear } from "https://cdn.skypack.dev/d3-scale@4";
-      import {
-        getModelInfo,
-        getEmbeddings,
-        getWikiText,
-        cosineSimilarity,
-      } from "./utils.js";
-      const bertWorker = new Worker("./bertWorker.js", {
-        type: "module",
-      });
-      const inputContainerEL = document.querySelector("#input-container");
-      const textAreaEl = document.querySelector("#input-area");
-      const outputAreaEl = document.querySelector("#output-area");
-      const formEl = document.querySelector("#form");
-      const searchInputEl = document.querySelector("#search-input");
-      const formWikiEl = document.querySelector("#form-wiki");
-      const searchWikiEl = document.querySelector("#search-wiki");
-      const outputStatusEl = document.querySelector("#output-status");
-      const modelSelectEl = document.querySelector("#model");
-      const sentencesRegex =
-        /(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<![A-Z]\.)(?<=\.|\?)\s/gm;
-      let sentenceEmbeddings = [];
-      let currInputText = "";
-      let isCalculating = false;
-      function toggleTextArea(state) {
-        if (state) {
-          textAreaEl.hidden = false;
-          textAreaEl.focus();
-        } else {
-          textAreaEl.hidden = true;
-        }
-      }
-      inputContainerEL.addEventListener("focus", (e) => {
-        toggleTextArea(true);
-      });
-      textAreaEl.addEventListener("blur", (e) => {
-        toggleTextArea(false);
-      });
-      textAreaEl.addEventListener("focusout", (e) => {
-        toggleTextArea(false);
-        if (currInputText === textAreaEl.value || isCalculating) return;
-        populateOutputArea(textAreaEl.value);
-        calculateEmbeddings(textAreaEl.value);
-      });
-      modelSelectEl.addEventListener("change", (e) => {
-        const query = new URLSearchParams(window.location.search);
-        query.set("model", modelSelectEl.value);
-        window.history.replaceState(
-          {},
-          "",
-          `${window.location.pathname}?${query}`
-        );
-        window.parent.postMessage({ queryString: "?" + queryString }, "*")
-        if (currInputText === "" || isCalculating) return;
-        populateOutputArea(textAreaEl.value);
-        calculateEmbeddings(textAreaEl.value);
-      });
-      function populateOutputArea(text) {
-        currInputText = text;
-        const sentences = text.split(sentencesRegex);
-        outputAreaEl.innerHTML = "";
-        for (const [id, sentence] of sentences.entries()) {
-          const sentenceEl = document.createElement("span");
-          sentenceEl.id = `sentence-${id}`;
-          sentenceEl.innerText = sentence + " ";
-          outputAreaEl.appendChild(sentenceEl);
-        }
-      }
-      formEl.addEventListener("submit", async (e) => {
-        e.preventDefault();
-        if (isCalculating || currInputText === "") return;
-        toggleInputs(true);
-        const modelID = modelSelectEl.value;
-        const { modelURL, tokenizerURL, configURL, search_prefix } =
-          getModelInfo(modelID);
-        const text = searchInputEl.value;
-        const query = search_prefix + searchInputEl.value;
-        outputStatusEl.classList.remove("invisible");
-        outputStatusEl.innerText = "Calculating embeddings for query...";
-        isCalculating = true;
-        const out = await getEmbeddings(
-          bertWorker,
-          modelURL,
-          tokenizerURL,
-          configURL,
-          modelID,
-          [query]
-        );
-        outputStatusEl.classList.add("invisible");
-        const queryEmbeddings = out.output[0];
-        // calculate cosine similarity with all sentences given the query
-        const distances = sentenceEmbeddings
-          .map((embedding, id) => ({
-            id,
-            similarity: cosineSimilarity(queryEmbeddings, embedding),
-          }))
-          .sort((a, b) => b.similarity - a.similarity)
-          // getting top 10 most similar sentences
-          .slice(0, 10);
-        const colorScale = scaleLinear()
-          .domain([
-            distances[distances.length - 1].similarity,
-            distances[0].similarity,
-          ])
-          .range([0, 1])
-          .interpolate(() => interpolateReds);
-        outputAreaEl.querySelectorAll("span").forEach((el) => {
-          el.style.color = "unset";
-          el.style.backgroundColor = "unset";
-        });
-        distances.forEach((d) => {
-          const el = outputAreaEl.querySelector(`#sentence-${d.id}`);
-          const color = colorScale(d.similarity);
-          const fontColor = hcl(color).l < 70 ? "white" : "black";
-          el.style.color = fontColor;
-          el.style.backgroundColor = color;
-        });
-        outputAreaEl
-          .querySelector(`#sentence-${distances[0].id}`)
-          .scrollIntoView({
-            behavior: "smooth",
-            block: "center",
-            inline: "nearest",
-          });
-        isCalculating = false;
-        toggleInputs(false);
-      });
-      async function calculateEmbeddings(text) {
-        isCalculating = true;
-        toggleInputs(true);
-        const modelID = modelSelectEl.value;
-        const { modelURL, tokenizerURL, configURL, document_prefix } =
-          getModelInfo(modelID);
-        const sentences = text.split(sentencesRegex);
-        const allEmbeddings = [];
-        outputStatusEl.classList.remove("invisible");
-        for (const [id, sentence] of sentences.entries()) {
-          const query = document_prefix + sentence;
-          outputStatusEl.innerText = `Calculating embeddings: sentence ${
-            id + 1
-          } of ${sentences.length}`;
-          const embeddings = await getEmbeddings(
-            bertWorker,
-            modelURL,
-            tokenizerURL,
-            configURL,
-            modelID,
-            [query],
-            updateStatus
-          );
-          allEmbeddings.push(embeddings);
-        }
-        outputStatusEl.classList.add("invisible");
-        sentenceEmbeddings = allEmbeddings.map((e) => e.output[0]);
-        isCalculating = false;
-        toggleInputs(false);
-      }
-      function updateStatus(data) {
-        if ("status" in data) {
-          if (data.status === "loading") {
-            outputStatusEl.innerText = data.message;
-            outputStatusEl.classList.remove("invisible");
-          }
         }
-      }
-      function toggleInputs(state) {
-        const interactive = document.querySelectorAll(".interactive");
-        interactive.forEach((el) => {
-          if (state) {
-            el.disabled = true;
-          } else {
-            el.disabled = false;
-          }
-        });
-      }
-      searchWikiEl.addEventListener("input", () => {
-        searchWikiEl.setCustomValidity("");
-      });
-      formWikiEl.addEventListener("submit", async (e) => {
-        e.preventDefault();
-        if ("example" in e.submitter.dataset) {
-          searchWikiEl.value = e.submitter.innerText;
         }
-        const text = searchWikiEl.value;
-        if (isCalculating || text === "") return;
-        try {
-          const wikiText = await getWikiText(text);
-          searchWikiEl.setCustomValidity("");
-          textAreaEl.innerHTML = wikiText;
-          populateOutputArea(wikiText);
-          calculateEmbeddings(wikiText);
-          searchWikiEl.value = "";
-        } catch {
-          searchWikiEl.setCustomValidity("Invalid Wikipedia article name");
-          searchWikiEl.reportValidity();
-        }
-      });
-      document.addEventListener("DOMContentLoaded", () => {
-        const query = new URLSearchParams(window.location.search);
-        const modelID = query.get("model");
-        if (modelID) {
-          modelSelectEl.value = modelID;
-          modelSelectEl.dispatchEvent(new Event("change"));
-        }
-      });
-    </script>
-  </head>
-  <body class="container max-w-4xl mx-auto p-4">
-    <main class="grid grid-cols-1 gap-5 relative">
-      <span class="absolute text-5xl -ml-[1em]"> 🕯️ </span>
-      <div>
-        <h1 class="text-5xl font-bold">Candle BERT</h1>
-        <h2 class="text-2xl font-bold">Rust/WASM Demo</h2>
-        <p class="max-w-lg">
-          Running sentence embeddings and similarity search in the browser using
-          the Bert Model written with
-          <a
-            href="https://github.com/huggingface/candle/"
-            target="_blank"
-            class="underline hover:text-blue-500 hover:no-underline"
-            >Candle
-          </a>
-          and compiled to Wasm. Embeddings models from are from
-          <a
-            href="https://huggingface.co/sentence-transformers/"
-            target="_blank"
-            class="underline hover:text-blue-500 hover:no-underline">
-            Sentence Transformers
-          </a>
-          and
-          <a
-            href="https://huggingface.co/intfloat/"
-            target="_blank"
-            class="underline hover:text-blue-500 hover:no-underline">
-            Liang Wang - e5 Models
-          </a>
-        </p>
-      </div>
-      <div>
-        <label for="model" class="font-medium block">Models Options: </label>
-        <select
-          id="model"
-          class="border-2 border-gray-500 rounded-md font-light interactive disabled:cursor-not-allowed w-full max-w-max">
-          <option value="gte_tiny">gte_tiny (45.5 MB)</option>
-          <option value="intfloat_e5_small_v2" selected>
-            intfloat/e5-small-v2 (133 MB)
-          </option>
-          <option value="intfloat_e5_base_v2">
-            intfloat/e5-base-v2 (438 MB)
-          </option>
-          <option value="intfloat_multilingual_e5_small">
-            intfloat/multilingual-e5-small (471 MB)
-          </option>
-          <option value="sentence_transformers_all_MiniLM_L6_v2">
-            sentence-transformers/all-MiniLM-L6-v2 (90.9 MB)
-          </option>
-          <option value="sentence_transformers_all_MiniLM_L12_v2">
-            sentence-transformers/all-MiniLM-L12-v2 (133 MB)
-          </option>
-        </select>
-      </div>
-      <div>
-        <h3 class="font-medium">Examples:</h3>
-        <form
-          id="form-wiki"
-          class="flex text-xs rounded-md justify-between w-min gap-3">
-          <input type="submit" hidden />
-          <button data-example class="disabled:cursor-not-allowed interactive">
-            Pizza
-          </button>
-          <button data-example class="disabled:cursor-not-allowed interactive">
-            Paris
-          </button>
-          <button data-example class="disabled:cursor-not-allowed interactive">
-            Physics
-          </button>
-          <input
-            type="text"
-            id="search-wiki"
-            title="Search Wikipedia article by title"
-            class="font-light py-0 mx-1 resize-none outline-none w-32 disabled:cursor-not-allowed interactive"
-            placeholder="Load Wikipedia article..." />
-          <button
-            title="Search Wikipedia article and load into input"
-            class="bg-gray-700 hover:bg-gray-800 text-white font-normal px-2 py-1 rounded disabled:bg-gray-300 disabled:cursor-not-allowed interactive">
-            Load
-          </button>
-        </form>
-      </div>
-      <form
-        id="form"
-        class="flex text-normal px-1 py-1 border border-gray-700 rounded-md items-center">
-        <input type="submit" hidden />
-        <input
-          type="text"
-          id="search-input"
-          class="font-light w-full px-3 py-2 mx-1 resize-none outline-none interactive disabled:cursor-not-allowed"
-          placeholder="Search query here..." />
-        <button
-          class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-2 w-16 rounded disabled:bg-gray-300 disabled:cursor-not-allowed interactive">
-          Search
-        </button>
-      </form>
-      <div>
-        <h3 class="font-medium">Input text:</h3>
-        <div class="flex justify-between items-center">
-          <div class="rounded-md inline text-xs">
-            <span id="output-status" class="m-auto font-light invisible"
-              >C</span
-            >
-          </div>
-        </div>
-        <div
-          id="input-container"
-          tabindex="0"
-          class="min-h-[250px] bg-slate-100 text-gray-500 rounded-md p-4 flex flex-col gap-2 relative">
-          <textarea
-            id="input-area"
-            hidden
-            value=""
-            placeholder="Input text to perform semantic similarity search..."
-            class="flex-1 resize-none outline-none left-0 right-0 top-0 bottom-0 m-4 absolute interactive disabled:invisible"></textarea>
-          <p id="output-area" class="grid-rows-2">
-            Input text to perform semantic similarity search...
-          </p>
-        </div>
-      </div>
-    </main>
-  </body>
-</html>

+import gradio as gr
+def predict(text, url_params):
+    print(url_params)
+    return ["Hello " + text + "!!", url_params]
+get_window_url_params = """
+    function(text_input, url_params) {
+        console.log(text_input, url_params);
+        const params = new URLSearchParams(window.location.search);
+        url_params = Object.fromEntries(params);
+        return [text_input, url_params];
         }
+    """
+set_window_url_params = """
+    function(text_input, url_params) {
+            const params = new URLSearchParams(window.location.search);
+            params.set("text_input", text_input)
+            url_params = Object.fromEntries(params);
+    	    const queryString = '?' + params.toString();
+            // this next line is only needed inside Spaces, so the child frame updates parent
+            window.parent.postMessage({ queryString: queryString }, "*")
+            return [text_input, url_params];
         }
+    """
+with gr.Blocks() as block:
+    url_params = gr.JSON({}, visible=True, label="URL Params")
+    text_input = gr.Text(label="Input")
+    text_output = gr.Text(label="Output")
+    btn = gr.Button("Get Params")
+    btn.click(fn=predict, inputs=[text_input, url_params],
+              outputs=[text_output, url_params], _js=get_window_url_params)
+    btn2 = gr.Button("Set Params")
+    btn2.click(fn=predict, inputs=[text_input, url_params],
+              outputs=[text_output, url_params], _js=set_window_url_params)
+    block.load(
+        fn=predict,
+        inputs=[text_input, url_params],
+        outputs=[text_output, url_params],
+        _js=get_window_url_params
+    )
+block.launch(debug=True)