use threading instead of process

build/lib/gradio/networking.py

@@ -6,7 +6,7 @@ import os
 import socket
 import threading
 from flask import Flask, request, jsonify, abort, send_file, render_template
-from multiprocessing import Process
+import threading
 import pkg_resources
 from distutils import dir_util
 import gradio as gr
@@ -35,8 +35,7 @@ app = Flask(__name__,
     template_folder=STATIC_TEMPLATE_LIB,
     static_folder=STATIC_PATH_LIB)
 app.app_globals = {}
-# app.config["FLASK_SKIP_DOTENV"] = 1
-# app.FLASK_SKIP_DOTENV = 1
+
 
 def set_meta_tags(title, description, thumbnail):
     app.app_globals.update({
@@ -159,7 +158,6 @@ def interpret():
 @app.route("/file/<path:path>", methods=["GET"])
 def file(path):
     return send_file(os.path.join(app.cwd, path))
-                
 
 def start_server(interface, server_port=None):
     if server_port is None:
@@ -169,7 +167,7 @@ def start_server(interface, server_port=None):
     )
     app.interface = interface
     app.cwd = os.getcwd()
-    process = Process(target=app.run, kwargs={"port": port})
+    process = threading.Thread(target=app.run, kwargs={"port": port})
     process.start()
     return port, app, process
 

demo/image_classifier.py

@@ -9,9 +9,8 @@ import requests
 from urllib.request import urlretrieve
 
 # # Download human-readable labels for ImageNet.
-# response = requests.get("https://git.io/JJkYN") 
-# labels = response.text.split("\n")
-labels = range(1000)  # comment this later
+response = requests.get("https://git.io/JJkYN") 
+labels = response.text.split("\n")
 
 mobile_net = tf.keras.applications.MobileNetV2()
 
@@ -22,27 +21,16 @@ def image_classifier(im):
     prediction = mobile_net.predict(arr).flatten()
     return {labels[i]: float(prediction[i]) for i in range(1000)}
 
-def image_explain(im):
-    model.layers[-1].activation = tf.keras.activations.linear
-    model = utils.apply_modifications(model)
-    penultimate_layer_idx =  2
-    class_idx  = class_idxs_sorted[0]
-    seed_input = img
-    grad_top1  = visualize_cam(model, layer_idx, class_idx, seed_input, 
-                            penultimate_layer_idx = penultimate_layer_idx,#None,
-                            backprop_modifier     = None,
-                            grad_modifier         = None)
-    print(grad_top_1)
-    return grad_top1
-
 
 image = gr.inputs.Image(shape=(224, 224))
 label = gr.outputs.Label(num_top_classes=3)
 
-gr.Interface(image_classifier, image, label,
+io = gr.Interface(image_classifier, image, label,
     capture_session=True,
     interpretation="default",
     examples=[
         ["images/cheetah1.jpg"],
         ["images/lion.jpg"]
-    ]).launch();
+    ])
+
+io.launch()

gradio/networking.py

@@ -6,7 +6,7 @@ import os
 import socket
 import threading
 from flask import Flask, request, jsonify, abort, send_file, render_template
-from multiprocessing import Process
+import threading
 import pkg_resources
 from distutils import dir_util
 import gradio as gr
@@ -158,7 +158,6 @@ def interpret():
 @app.route("/file/<path:path>", methods=["GET"])
 def file(path):
     return send_file(os.path.join(app.cwd, path))
-                
 
 def start_server(interface, server_port=None):
     if server_port is None:
@@ -168,7 +167,7 @@ def start_server(interface, server_port=None):
     )
     app.interface = interface
     app.cwd = os.getcwd()
-    process = Process(target=app.run, kwargs={"port": port})
+    process = threading.Thread(target=app.run, kwargs={"port": port})
     process.start()
     return port, app, process