updated PyPi version

demo/calculator.py

@@ -1,9 +1,6 @@
 import gradio as gr
-import random
-import time
 
 def calculator(num1, operation, num2):
-    print(num1, operation, num2)
     if operation == "add":
         return num1 + num2
     elif operation == "subtract":

frontend/src/gradio.jsx

@@ -60,8 +60,8 @@ export class GradioInterface extends React.Component {
       process.env.REACT_APP_BACKEND_URL +
       (this.props.examples_dir === null
         ? "file" +
-        this.props.examples_dir +
-        (this.props.examples_dir.endswith("/") ? "" : "/")
+          this.props.examples_dir +
+          (this.props.examples_dir.endswith("/") ? "" : "/")
         : "file");
   }
   get_default_state = () => {
@@ -184,8 +184,8 @@ export class GradioInterface extends React.Component {
     this.setState({ interpretation: null });
   };
   queueCallback = (queue_index) => {
-    this.setState({"queue_index": queue_index});
-  }
+    this.setState({ queue_index: queue_index });
+  };
   takeScreenshot = () => {
     html2canvas(ReactDOM.findDOMNode(this).parentNode).then((canvas) => {
       saveAs(canvas.toDataURL(), "screenshot.png");
@@ -207,10 +207,10 @@ export class GradioInterface extends React.Component {
         i < this.props.input_components.length
           ? input_component_map[this.props.input_components[i].name][1]
           : output_component_map[
-          this.props.output_components[
-            i - this.props.input_components.length
-          ].name
-          ][1];
+              this.props.output_components[
+                i - this.props.input_components.length
+              ].name
+            ][1];
       state_change[i] = ExampleComponent.preprocess(
         item,
         this.examples_dir
@@ -224,7 +224,9 @@ export class GradioInterface extends React.Component {
     if (this.state.submitting) {
       status = (
         <div className="loading">
-          {this.state.queue_index !== null ? "queued @ " + this.state.queue_index : false}
+          {this.state.queue_index !== null
+            ? "queued @ " + this.state.queue_index
+            : false}
           <img alt="loading" src={logo_loading} />
         </div>
       );
@@ -425,14 +427,14 @@ class GradioInterfaceExamples extends React.Component {
                 return <th key={i}>{component.label}</th>;
               })}
               {this.props.examples[0].length >
-                this.props.input_components.length
+              this.props.input_components.length
                 ? this.props.output_components.map((component, i) => {
-                  return (
-                    <th key={i + this.props.input_components.length}>
-                      {component.label}
-                    </th>
-                  );
-                })
+                    return (
+                      <th key={i + this.props.input_components.length}>
+                        {component.label}
+                      </th>
+                    );
+                  })
                 : false}
             </tr>
           </thead>
@@ -449,7 +451,7 @@ class GradioInterfaceExamples extends React.Component {
                   {example_row.map((example_data, j) => {
                     let ExampleComponent =
                       input_component_map[
-                      this.props.input_components[j].name
+                        this.props.input_components[j].name
                       ][1];
                     return (
                       <td>

frontend/src/index.jsx

@@ -4,37 +4,35 @@ import { GradioPage } from "./gradio";
 import Login from "./login";
 
 function delay(n) {
-  return new Promise(function(resolve){
-      setTimeout(resolve,n*1000);
+  return new Promise(function (resolve) {
+    setTimeout(resolve, n * 1000);
   });
 }
 
 let postData = async (url, body) => {
-  const output = await fetch(
-    process.env.REACT_APP_BACKEND_URL + url,
-    {
-      method: "POST",
-      body: JSON.stringify(body),
-      headers: {"Content-Type": "application/json"}
-    }
-  );
+  const output = await fetch(process.env.REACT_APP_BACKEND_URL + url, {
+    method: "POST",
+    body: JSON.stringify(body),
+    headers: { "Content-Type": "application/json" }
+  });
   return output;
-}
+};
 
 let fn = async (queue, data, action, queue_callback) => {
   if (queue && ["predict", "interpret"].includes(action)) {
-    const output = await postData(
-      "api/queue/push/", { data: data, action:action },
-    );
+    const output = await postData("api/queue/push/", {
+      data: data,
+      action: action
+    });
     let hash = await output.text();
     let status = "UNKNOWN";
     while (status != "COMPLETE" && status != "FAILED") {
       if (status != "UNKNOWN") {
         await delay(1);
       }
-      const status_response = await postData(
-        "api/queue/status/", { hash: hash },
-      );
+      const status_response = await postData("api/queue/status/", {
+        hash: hash
+      });
       var status_obj = await status_response.json();
       status = status_obj["status"];
       if (status === "QUEUED") {
@@ -49,10 +47,8 @@ let fn = async (queue, data, action, queue_callback) => {
       return status_obj["data"];
     }
   } else {
-    const output = await postData(
-      "api/" + action + "/", { data: data },
-    );
-    return await output.json();  
+    const output = await postData("api/" + action + "/", { data: data });
+    return await output.json();
   }
 };
 

frontend/src/login.jsx

@@ -1,25 +1,29 @@
 import("./themes/defaults.scss");
 
 export default function Login(props) {
-  return (
-    <div class="login">
-      <form
-        id="login"
-        method="POST"
-        action={process.env.REACT_APP_BACKEND_URL + "login"}
-      >
-        <h2>login</h2>
-        {props.auth_message ? (
-          <div dangerouslySetInnerHTML={{ __html: props.auth_message }}></div>
-        ) : (
-          false
-        )}
-        <label for="username">username</label>
-        <input type="text" name="username"></input>
-        <label for="password">password</label>
-        <input type="password" name="password"></input>
-        <input class="link" type="submit" className="submit"></input>
-      </form>
-    </div>
-  );
+  if (props.auth_message && props.auth_message.trim().startsWith("<main")) {
+    return <div dangerouslySetInnerHTML={{ __html: props.auth_message }}></div>;
+  } else {
+    return (
+      <div class="login">
+        <form
+          id="login"
+          method="POST"
+          action={process.env.REACT_APP_BACKEND_URL + "login"}
+        >
+          <h2>login</h2>
+          {props.auth_message ? (
+            <div dangerouslySetInnerHTML={{ __html: props.auth_message }}></div>
+          ) : (
+            false
+          )}
+          <label for="username">username</label>
+          <input type="text" name="username"></input>
+          <label for="password">password</label>
+          <input type="password" name="password"></input>
+          <input class="link" type="submit" className="submit"></input>
+        </form>
+      </div>
+    );
+  }
 }

gradio.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 1.0
 Name: gradio
-Version: 2.2.12
+Version: 2.2.13
 Summary: Python library for easily interacting with trained machine learning models
 Home-page: https://github.com/gradio-app/gradio-UI
 Author: Abubakar Abid

gradio/version.txt

@@ -1 +1 @@
-2.2.12
+2.2.13

setup.py

@@ -5,7 +5,7 @@ except ImportError:
 
 setup(
     name='gradio',
-    version='2.2.12',
+    version='2.2.13',
     include_package_data=True,
     description='Python library for easily interacting with trained machine learning models',
     author='Abubakar Abid',