Website: WIP (gradio-app#328)

Ported gradio website into gradio repository, now launched as a docker service from gradio/website

Author: aliabid94

.dockerignore

@@ -0,0 +1,39 @@
+# Python build
+.eggs/
+gradio.egg-info/*
+!gradio.egg-info/requires.txt
+!gradio.egg-info/PKG-INFO
+dist/
+*.pyc
+__pycache__/
+*.py[cod]
+*$py.class
+build/
+
+# JS build
+gradio/templates/frontend/static
+
+# Secrets
+.env
+
+# Gradio run artifacts
+*.db
+*.sqlite3
+gradio/launches.json
+
+# Tests
+.coverage
+coverage.xml
+test.txt
+
+# Demos
+demo/tmp.zip
+demo/flagged
+demo/files/*.avi
+demo/files/*.mp4
+
+# Etc
+.idea/*
+.DS_Store
+*.bak
+workspace.code-workspace

.gitignore

@@ -1,40 +1,40 @@
-venv
-dist/
+# Python build
 .eggs/
 gradio.egg-info/*
 !gradio.egg-info/requires.txt
 !gradio.egg-info/PKG-INFO
+dist/
 *.pyc
-staticfiles
-.env
-.coverage
-coverage.xml
-*.sqlite3
-.idea/*
-*.ipynb
-.ipynb_checkpoints/*
-models/*
-.models/*
-gradio_files/*
-ngrok*
-examples/ngrok*
-gradio-flagged/*
-.DS_Store
 __pycache__/
 *.py[cod]
 *$py.class
-demo/models/*
-*.h5
-docs.json
-*.bak
-demo/tmp.zip
-demo/flagged
-test.txt
 build/
-flagged/
-gradio/launches.json
+
+# JS build
 gradio/templates/frontend/static
-workspace.code-workspace
+
+# Secrets
+.env
+
+# Gradio run artifacts
 *.db
+*.sqlite3
+gradio/launches.json
+flagged
+
+# Tests
+.coverage
+coverage.xml
+test.txt
+
+# Demos
+demo/tmp.zip
 demo/files/*.avi
 demo/files/*.mp4
+
+# Etc
+.idea/*
+.DS_Store
+*.bak
+workspace.code-workspace
+*.h5

README.md

@@ -0,0 +1 @@
+tensorflow

demo/calculator.py renamed to demo/calculator/run.py

@@ -0,0 +1 @@
+fpdf

demo/screenshots/calculator/1.gif renamed to demo/calculator/screenshot.gif

@@ -35,7 +35,7 @@ def disease_report(img, scan_for, generate_report):
     ],
     title="Disease Report",
     description="Upload an Xray and select the diseases to scan for.",
-    theme="compact",
+    theme="grass",
     flagging_options=["good", "bad", "etc"],
     allow_flagging="auto"
 )

demo/calculator_live.py renamed to demo/calculator_live/run.py

@@ -0,0 +1 @@
+matplotlib

demo/screenshots/calculator_live/1.gif renamed to demo/calculator_live/screenshot.gif

@@ -0,0 +1 @@
+pandas

demo/chatbot.py renamed to demo/chatbot/run.py

@@ -0,0 +1 @@
+numpy

demo/screenshots/chatbot/1.gif renamed to demo/chatbot/screenshot.gif

@@ -0,0 +1,13 @@
+import gradio as gr
+
+title = "GPT-J-6B"
+
+examples = [
+    ['The tower is 324 metres (1,063 ft) tall,'],
+    ["The Moon's orbit around Earth has"],
+    ["The smooth Borealis basin in the Northern Hemisphere covers 40%"]
+]
+
+gr.Interface.load("huggingface/EleutherAI/gpt-j-6B", 
+    inputs=gr.inputs.Textbox(lines=5, label="Input Text"),
+    title=title, examples=examples).launch();

demo/diff_texts.py renamed to demo/diff_texts/run.py

@@ -0,0 +1,2 @@
+numpy
+tensorflow

demo/screenshots/diff_texts/1.png renamed to demo/diff_texts/screenshot.png

@@ -0,0 +1,20 @@
+import gradio as gr
+import tensorflow as tf
+import requests
+
+inception_net = tf.keras.applications.MobileNetV2() # load the model
+
+# Download human-readable labels for ImageNet.
+response = requests.get("https://git.io/JJkYN")
+labels = response.text.split("\n")
+
+def classify_image(inp):
+  inp = inp.reshape((-1, 224, 224, 3))
+  inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
+  prediction = inception_net.predict(inp).flatten()
+  return {labels[i]: float(prediction[i]) for i in range(1000)}
+
+image = gr.inputs.Image(shape=(224, 224))
+label = gr.outputs.Label(num_top_classes=3)
+
+gr.Interface(fn=classify_image, inputs=image, outputs=label).launch()