Merge branch 'blocks-tests' of https://github.com/gradio-app/gradio into blocks-tests

demo/chatbot/run.py

@@ -17,7 +17,7 @@ def chat(message, history):
     return history, history
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     chat,
     ["text", "state"],
     ["chatbot", "state"],
@@ -25,4 +25,4 @@ iface = gr.Interface(
     allow_flagging="never",
 )
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/diff_texts/run.py

@@ -11,15 +11,15 @@ def diff_texts(text1, text2):
     ]
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     diff_texts,
     [
-        gr.inputs.Textbox(
+        gr.Textbox(
             lines=3, default="The quick brown fox jumped over the lazy dogs."
         ),
-        gr.inputs.Textbox(lines=3, default="The fast brown fox jumps over lazy dogs."),
+        gr.Textbox(lines=3, default="The fast brown fox jumps over lazy dogs."),
     ],
-    gr.outputs.HighlightedText(color_map={"+": "green", "-": "pink"}),
+    gr.HighlightedText(),
 )
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/digit_classifier/run.py

@@ -18,20 +18,18 @@ def recognize_digit(image):
     return {str(i): prediction[i] for i in range(10)}
 
 
-im = gradio.inputs.Image(
+im = gradio.Image(
     shape=(28, 28), image_mode="L", invert_colors=False, source="canvas"
 )
 
-iface = gr.Interface(
+demo = gr.Interface(
     recognize_digit,
     im,
-    gradio.outputs.Label(num_top_classes=3),
+    gradio.Label(num_top_classes=3),
     live=True,
     interpretation="default",
     capture_session=True,
 )
 
-iface.test_launch()
-
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/disease_report/run.py

@@ -25,18 +25,18 @@ def disease_report(img, scan_for, generate_report):
     return results, report if generate_report else None
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     disease_report,
     [
         "image",
-        gr.inputs.CheckboxGroup(
+        gr.CheckboxGroup(
             ["Cancer", "Rash", "Heart Failure", "Stroke", "Diabetes", "Pneumonia"]
         ),
         "checkbox",
     ],
     [
-        gr.outputs.Carousel(["text", "image"], label="Disease"),
-        gr.outputs.File(label="Report"),
+        gr.Carousel(["text", "image"], label="Disease"),
+        gr.File(label="Report"),
     ],
     title="Disease Report",
     description="Upload an Xray and select the diseases to scan for.",
@@ -46,4 +46,4 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/filter_records/run.py

@@ -5,21 +5,19 @@ def filter_records(records, gender):
     return records[records["gender"] == gender]
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     filter_records,
     [
-        gr.inputs.Dataframe(
+        gr.Dataframe(
             headers=["name", "age", "gender"],
             datatype=["str", "number", "str"],
             row_count=5,
         ),
-        gr.inputs.Dropdown(["M", "F", "O"]),
+        gr.Dropdown(["M", "F", "O"]),
     ],
     "dataframe",
     description="Enter gender as 'M', 'F', or 'O' for other.",
 )
 
-iface.test_launch()
-
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/form_graph/run.py

@@ -1,5 +1,3 @@
-import random
-
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -24,19 +22,19 @@ def plot_forecast(final_year, companies, noise, show_legend, point_style):
     return fig
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     plot_forecast,
     [
-        gr.inputs.Radio([2025, 2030, 2035, 2040], label="Project to:"),
-        gr.inputs.CheckboxGroup(
+        gr.Radio([2025, 2030, 2035, 2040], label="Project to:"),
+        gr.CheckboxGroup(
             ["Google", "Microsoft", "Gradio"], label="Company Selection"
         ),
-        gr.inputs.Slider(1, 100, label="Noise Level"),
-        gr.inputs.Checkbox(label="Show Legend"),
-        gr.inputs.Dropdown(["cross", "line", "circle"], label="Style"),
+        gr.Slider(minimum=1, maximum=100, label="Noise Level"),
+        gr.Checkbox(label="Show Legend"),
+        gr.Dropdown(["cross", "line", "circle"], label="Style"),
     ],
-    gr.outputs.Image(plot=True, label="forecast"),
+    gr.Image(plot=True, label="forecast"),
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/fraud_detector/run.py

@@ -1,7 +1,5 @@
 import random
 
-import pandas as pd
-
 import gradio as gr
 
 
@@ -19,20 +17,20 @@ def fraud_detector(card_activity, categories, sensitivity):
     )
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     fraud_detector,
     [
-        gr.inputs.Timeseries(x="time", y=["retail", "food", "other"]),
-        gr.inputs.CheckboxGroup(
-            ["retail", "food", "other"], default=["retail", "food", "other"]
+        gr.Timeseries(x="time", y=["retail", "food", "other"]),
+        gr.CheckboxGroup(
+            ["retail", "food", "other"], default_selected=["retail", "food", "other"]
         ),
-        gr.inputs.Slider(1, 3),
+        gr.Slider(minimum=1, maximum=3),
     ],
     [
         "dataframe",
-        gr.outputs.Timeseries(x="time", y=["retail", "food", "other"]),
-        gr.outputs.Label(label="Fraud Level"),
+        gr.Timeseries(x="time", y=["retail", "food", "other"]),
+        gr.Label(label="Fraud Level"),
     ],
 )
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/gender_sentence_custom_interpretation/run.py

@@ -31,12 +31,12 @@ def interpret_gender(sentence):
     return interpretation
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     fn=gender_of_sentence,
-    inputs=gr.inputs.Textbox(default="She went to his house to get her keys."),
+    inputs=gr.Textbox(default="She went to his house to get her keys."),
     outputs="label",
-    interpretation=interpret_gender,
-    enable_queue=True,
+    interpretation=interpret_gender
 )
+
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch(auth=("a", "b"))

demo/gender_sentence_default_interpretation/run.py

@@ -1,5 +1,3 @@
-import re
-
 import gradio as gr
 
 male_words, female_words = ["he", "his", "him"], ["she", "hers", "her"]
@@ -14,11 +12,12 @@ def gender_of_sentence(sentence):
     return {"male": male_count / total, "female": female_count / total}
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     fn=gender_of_sentence,
-    inputs=gr.inputs.Textbox(default="She went to his house to get her keys."),
+    inputs=gr.Textbox(default="She went to his house to get her keys."),
     outputs="label",
     interpretation="default",
 )
+
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/generate_tone/run.py

@@ -15,7 +15,7 @@ def generate_tone(note, octave, duration):
     return sr, audio
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     generate_tone,
     [
         gr.inputs.Dropdown(notes, type="index"),
@@ -26,4 +26,4 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/gpt_j/run.py

@@ -8,9 +8,12 @@ examples = [
     ["The smooth Borealis basin in the Northern Hemisphere covers 40%"],
 ]
 
-gr.Interface.load(
+demo = gr.Interface.load(
     "huggingface/EleutherAI/gpt-j-6B",
-    inputs=gr.inputs.Textbox(lines=5, label="Input Text"),
+    inputs=gr.Textbox(lines=5, label="Input Text"),
     title=title,
     examples=examples,
-).launch()
+)
+
+if __name__ == "__main__":
+    demo.launch()

demo/hello_login/run.py

@@ -7,6 +7,6 @@ def greet(name):
     return "Hello " + name + "!!"
 
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
 if __name__ == "__main__":
-    iface.launch(auth=lambda u, p: user_db.get(u) == p)
+    demo.launch(auth=lambda u, p: user_db.get(u) == p)

demo/hello_world/run.py

@@ -5,6 +5,7 @@ def greet(name):
     return "Hello " + name + "!!"
 
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/hello_world_2/run.py

@@ -5,10 +5,11 @@ def greet(name):
     return "Hello " + name + "!"
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     fn=greet,
-    inputs=gr.inputs.Textbox(lines=2, placeholder="Name Here..."),
+    inputs=gr.Textbox(lines=2, placeholder="Name Here..."),
     outputs="text",
 )
+
 if __name__ == "__main__":
-    app, local_url, share_url = iface.launch()
+    app, local_url, share_url = demo.launch()

demo/hello_world_3/run.py

@@ -8,10 +8,10 @@ def greet(name, is_morning, temperature):
     return greeting, round(celsius, 2)
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     fn=greet,
-    inputs=["text", "checkbox", gr.inputs.Slider(0, 100)],
+    inputs=["text", "checkbox", gr.Slider(0, 100)],
     outputs=["text", "number"],
 )
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/image_classifier/run.py

@@ -17,12 +17,15 @@ def classify_image(inp):
     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)
+image = gr.Image(shape=(224, 224))
+label = gr.Label(num_top_classes=3)
 
-gr.Interface(
+demo = gr.Interface(
     fn=classify_image,
     inputs=image,
     outputs=label,
     examples=[["images/cheetah1.jpg"], ["images/lion.jpg"]],
-).launch()
+)
+
+if __name__ == "__main__":
+    demo.launch()

demo/image_classifier_2/run.py

@@ -20,6 +20,10 @@ def predict(inp):
     return {labels[i]: float(prediction[i]) for i in range(1000)}
 
 
-inputs = gr.inputs.Image()
-outputs = gr.outputs.Label(num_top_classes=3)
-gr.Interface(fn=predict, inputs=inputs, outputs=outputs).launch()
+inputs = gr.Image()
+outputs = gr.Label(num_top_classes=3)
+
+demo = gr.Interface(fn=predict, inputs=inputs, outputs=outputs)
+
+if __name__ == "__main__":
+    demo.launch()

demo/image_classifier_interpretation/run.py

@@ -17,9 +17,11 @@ def classify_image(inp):
     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)
+image = gr.Image(shape=(224, 224))
+label = gr.Label(num_top_classes=3)
 
-gr.Interface(
-    fn=classify_image, inputs=image, outputs=label, interpretation="default"
-).launch()
+demo = gr.Interface(fn=classify_image, inputs=image, outputs=label, 
+                    interpretation="default")
+
+if __name__ == "__main__":
+    demo.launch()

demo/image_mod/run.py

@@ -5,6 +5,7 @@ def image_mod(image):
     return image.rotate(45)
 
 
-iface = gr.Interface(image_mod, gr.inputs.Image(type="pil"), "image")
+demo = gr.Interface(image_mod, gr.inputs.Image(type="pil"), "image")
+
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/input-output/run.py

@@ -4,13 +4,14 @@ import gradio as gr
 def image_mod(text):
     return text[::-1]
 
-block = gr.Blocks()
+demo = gr.Blocks()
 
-with block:
+with demo:
     text = gr.Textbox()
     btn = gr.Button("Run")
     btn.click(image_mod, text, text)
 
-print(block.get_config_file())
+print(demo.get_config_file())
+
 if __name__ == "__main__":
-    block.launch()
+    demo.launch()

demo/kitchen_sink/run.py

@@ -82,48 +82,48 @@ def fn(
     )
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     fn,
     inputs=[
-        gr.inputs.Textbox(default="Lorem ipsum", label="Textbox"),
-        gr.inputs.Textbox(lines=3, placeholder="Type here..", label="Textbox 2"),
-        gr.inputs.Number(label="Number", default=42),
-        gr.inputs.Slider(minimum=10, maximum=20, default=15, label="Slider: 10 - 20"),
-        gr.inputs.Slider(maximum=20, step=0.04, label="Slider: step @ 0.04"),
-        gr.inputs.Checkbox(label="Checkbox"),
-        gr.inputs.CheckboxGroup(
-            label="CheckboxGroup", choices=CHOICES, default=CHOICES[0:2]
+        gr.Textbox(default_value="Lorem ipsum", label="Textbox"),
+        gr.Textbox(lines=3, placeholder="Type here..", label="Textbox 2"),
+        gr.Number(label="Number", default=42),
+        gr.Slider(minimum=10, maximum=20, default_value=15, label="Slider: 10 - 20"),
+        gr.Slider(maximum=20, step=0.04, label="Slider: step @ 0.04"),
+        gr.Checkbox(label="Checkbox"),
+        gr.CheckboxGroup(
+            label="CheckboxGroup", choices=CHOICES, default_selected=CHOICES[0:2]
         ),
-        gr.inputs.Radio(label="Radio", choices=CHOICES, default=CHOICES[2]),
-        gr.inputs.Dropdown(label="Dropdown", choices=CHOICES),
-        gr.inputs.Image(label="Image", optional=True),
-        gr.inputs.Image(label="Image w/ Cropper", tool="select", optional=True),
-        gr.inputs.Image(label="Sketchpad", source="canvas", optional=True),
-        gr.inputs.Image(label="Webcam", source="webcam", optional=True),
-        gr.inputs.Video(label="Video", optional=True),
-        gr.inputs.Audio(label="Audio", optional=True),
-        gr.inputs.Audio(label="Microphone", source="microphone", optional=True),
-        gr.inputs.File(label="File", optional=True),
-        gr.inputs.Dataframe(label="Dataframe", headers=["Name", "Age", "Gender"]),
-        gr.inputs.Timeseries(x="time", y=["price", "value"], optional=True),
+        gr.Radio(label="Radio", choices=CHOICES, default_selected=CHOICES[2]),
+        gr.Dropdown(label="Dropdown", choices=CHOICES),
+        gr.Image(label="Image"),
+        gr.Image(label="Image w/ Cropper", tool="select"),
+        gr.Image(label="Sketchpad", source="canvas"),
+        gr.Image(label="Webcam", source="webcam"),
+        gr.Video(label="Video"),
+        gr.Audio(label="Audio"),
+        gr.Audio(label="Microphone", source="microphone"),
+        gr.File(label="File"),
+        gr.Dataframe(label="Dataframe", headers=["Name", "Age", "Gender"]),
+        gr.Timeseries(x="time", y=["price", "value"]),
     ],
     outputs=[
-        gr.outputs.Textbox(label="Textbox"),
-        gr.outputs.Label(label="Label"),
-        gr.outputs.Audio(label="Audio"),
-        gr.outputs.Image(label="Image"),
-        gr.outputs.Video(label="Video"),
-        gr.outputs.HighlightedText(
+        gr.Textbox(label="Textbox"),
+        gr.Label(label="Label"),
+        gr.Audio(label="Audio"),
+        gr.Image(label="Image"),
+        gr.Video(label="Video"),
+        gr.HighlightedText(
             label="HighlightedText", color_map={"punc": "pink", "test 0": "blue"}
         ),
-        gr.outputs.HighlightedText(label="HighlightedText", show_legend=True),
-        gr.outputs.JSON(label="JSON"),
-        gr.outputs.HTML(label="HTML"),
-        gr.outputs.File(label="File"),
-        gr.outputs.Dataframe(label="Dataframe"),
-        gr.outputs.Dataframe(label="Numpy", type="numpy"),
-        gr.outputs.Carousel("image", label="Carousel"),
-        gr.outputs.Timeseries(x="time", y=["price", "value"], label="Timeseries"),
+        gr.HighlightedText(label="HighlightedText", show_legend=True),
+        gr.JSON(label="JSON"),
+        gr.HTML(label="HTML"),
+        gr.File(label="File"),
+        gr.Dataframe(label="Dataframe"),
+        gr.Dataframe(label="Numpy"),
+        gr.Carousel(components="image", label="Carousel"),
+        gr.Timeseries(x="time", y=["price", "value"], label="Timeseries"),
     ],
     examples=[
         [
@@ -156,4 +156,4 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/longest_word/run.py

@@ -9,11 +9,10 @@ def longest_word(text):
 
 ex = "The quick brown fox jumped over the lazy dog."
 
-iface = gr.Interface(
+demo = gr.Interface(
     longest_word, "textbox", "label", interpretation="default", examples=[[ex]]
 )
 
-iface.test_launch()
 
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/main_note/run.py

@@ -37,13 +37,14 @@ def main_note(audio):
         if pitch not in volume_per_pitch:
             volume_per_pitch[pitch] = 0
         volume_per_pitch[pitch] += 1.0 * volume / total_volume
+    volume_per_pitch = {k:float(v) for k,v in volume_per_pitch.items()}
     return volume_per_pitch
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     main_note,
-    "audio",
-    gr.outputs.Label(num_top_classes=4),
+    gr.Audio(source="microphone"),
+    gr.Label(num_top_classes=4),
     examples=[
         ["audio/recording1.wav"],
         ["audio/cantina.wav"],
@@ -52,4 +53,4 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/matrix_transpose/run.py

@@ -7,9 +7,9 @@ def transpose(matrix):
     return matrix.T
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     transpose,
-    gr.inputs.Dataframe(type="numpy", datatype="number", row_count=5, col_count=3),
+    gr.Dataframe(type="numpy", datatype="number", row_count=5, col_count=3),
     "numpy",
     examples=[
         [np.zeros((3, 3)).tolist()],
@@ -20,7 +20,5 @@ iface = gr.Interface(
     ],
 )
 
-iface.test_launch()
-
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/outbreak_forecast/run.py

@@ -27,17 +27,17 @@ def outbreak(r, month, countries, social_distancing):
     return plt
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     outbreak,
     [
-        gr.inputs.Slider(1, 4, default=3.2, label="R"),
-        gr.inputs.Dropdown(
+        gr.Slider(minimum=1, maximum=4, default_value=3.2, label="R"),
+        gr.Dropdown(
             ["January", "February", "March", "April", "May"], label="Month"
         ),
-        gr.inputs.CheckboxGroup(["USA", "Canada", "Mexico", "UK"], label="Countries"),
-        gr.inputs.Checkbox(label="Social Distancing?"),
+        gr.CheckboxGroup(["USA", "Canada", "Mexico", "UK"], label="Countries"),
+        gr.Checkbox(label="Social Distancing?"),
     ],
     "plot",
 )
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/question_answer/run.py

@@ -7,18 +7,21 @@ examples = [
     ]
 ]
 
-gr.Interface.load(
+demo = gr.Interface.load(
     "huggingface/deepset/roberta-base-squad2",
     inputs=[
-        gr.inputs.Textbox(
+        gr.Textbox(
             lines=5, label="Context", placeholder="Type a sentence or paragraph here."
         ),
-        gr.inputs.Textbox(
+        gr.Textbox(
             lines=2,
             label="Question",
             placeholder="Ask a question based on the context.",
         ),
     ],
-    outputs=[gr.outputs.Textbox(label="Answer"), gr.outputs.Label(label="Probability")],
+    outputs=[gr.Textbox(label="Answer"), gr.Label(label="Probability")],
     examples=examples,
-).launch()
+)
+
+if __name__ == "__main__":
+    demo.launch()

demo/reverse_audio/run.py

@@ -8,7 +8,7 @@ def reverse_audio(audio):
     return (sr, np.flipud(data))
 
 
-iface = gr.Interface(reverse_audio, "microphone", "audio", examples="audio")
+demo = gr.Interface(reverse_audio, "microphone", "audio", examples="audio")
 
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/sales_projections/run.py

@@ -24,9 +24,9 @@ def sales_projections(employee_data):
     return employee_data, plt.gcf(), regression_values
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     sales_projections,
-    gr.inputs.Dataframe(
+    gr.Dataframe(
         headers=["Name", "Jan Sales", "Feb Sales", "Mar Sales"],
         default=[["Jon", 12, 14, 18], ["Alice", 14, 17, 2], ["Sana", 8, 9.5, 12]],
     ),
@@ -34,4 +34,4 @@ iface = gr.Interface(
     description="Enter sales figures for employees to predict sales trajectory over year.",
 )
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/sentence_builder/run.py

@@ -5,14 +5,14 @@ def sentence_builder(quantity, animal, place, activity_list, morning):
     return f"""The {quantity} {animal}s went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     sentence_builder,
     [
-        gr.inputs.Slider(2, 20),
-        gr.inputs.Dropdown(["cat", "dog", "bird"]),
-        gr.inputs.Radio(["park", "zoo", "road"]),
-        gr.inputs.CheckboxGroup(["ran", "swam", "ate", "slept"]),
-        gr.inputs.Checkbox(label="Is it the morning?"),
+        gr.Slider(minimum=2, maximum=20),
+        gr.Dropdown(["cat", "dog", "bird"]),
+        gr.Radio(["park", "zoo", "road"]),
+        gr.CheckboxGroup(["ran", "swam", "ate", "slept"]),
+        gr.Checkbox(label="Is it the morning?"),
     ],
     "text",
     examples=[
@@ -24,4 +24,4 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/sentiment_analysis/run.py

@@ -13,8 +13,7 @@ def sentiment_analysis(text):
     return scores
 
 
-iface = gr.Interface(sentiment_analysis, "textbox", "label", interpretation="default")
+demo = gr.Interface(sentiment_analysis, "textbox", "label", interpretation="default")
 
-iface.test_launch()
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/sepia_filter/run.py

@@ -12,7 +12,7 @@ def sepia(input_img):
     return sepia_img
 
 
-iface = gr.Interface(sepia, gr.inputs.Image(shape=(200, 200)), "image")
+demo = gr.Interface(sepia, gr.Image(shape=(200, 200)), "image")
 
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/spectogram/run.py

@@ -16,8 +16,7 @@ def spectrogram(audio):
     return plt
 
 
-iface = gr.Interface(spectrogram, "audio", "plot")
+demo = gr.Interface(spectrogram, "audio", "plot")
 
-iface.test_launch()
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/speech_text_length_blocks/run.py

@@ -0,0 +1,29 @@
+from transformers import pipeline
+
+import gradio as gr
+
+asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
+classifier = pipeline("text-classification")
+
+def speech_to_text(speech):
+    text = asr(speech)["text"]
+    return text
+
+def text_to_sentiment(text):
+    return classifier(text)[0]["label"]
+
+demo = gr.Blocks()
+
+with demo:
+    m = gr.Audio(type="filepath")
+    t = gr.Textbox()
+    l = gr.Label()
+    
+    b1 = gr.Button("Recognize Speech")
+    b2 = gr.Button("Classify Sentiment")
+    
+    b1.click(speech_to_text, inputs=m, outputs=t)
+    b2.click(text_to_sentiment, inputs=t, outputs=l)
+
+if __name__ == "__main__":
+    demo.launch()

demo/stock_forecast/run.py

@@ -22,18 +22,17 @@ def stock_forecast(final_year, companies, noise, show_legend, point_style):
     return fig
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     stock_forecast,
     [
-        gr.inputs.Radio([2025, 2030, 2035, 2040], label="Project to:"),
-        gr.inputs.CheckboxGroup(["Google", "Microsoft", "Gradio"]),
-        gr.inputs.Slider(1, 100),
+        gr.Radio([2025, 2030, 2035, 2040], label="Project to:"),
+        gr.CheckboxGroup(["Google", "Microsoft", "Gradio"]),
+        gr.Slider(minimum=1, maximum=100),
         "checkbox",
-        gr.inputs.Dropdown(["cross", "line", "circle"], label="Style"),
+        gr.Dropdown(["cross", "line", "circle"], label="Style"),
     ],
-    gr.outputs.Image(plot=True, label="forecast"),
+    gr.Image(plot=True, label="forecast"),
 )
 
-iface.test_launch()
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/tax_calculator/run.py

@@ -19,19 +19,18 @@ def tax_calculator(income, marital_status, assets):
     return round(total_tax)
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     tax_calculator,
     [
         "number",
-        gr.inputs.Radio(["Single", "Married", "Divorced"]),
-        gr.inputs.Dataframe(
+        gr.Radio(["Single", "Married", "Divorced"]),
+        gr.Dataframe(
             headers=["Item", "Cost", "Deduct"],
             datatype=["str", "number", "bool"],
             label="Assets Purchased this Year",
         ),
     ],
     "number",
-    # interpretation="default",  # Removed interpretation for dataframes
     examples=[
         [10000, "Married", [["Car", 5000, False], ["Laptop", 800, True]]],
         [80000, "Single", [["Suit", 800, True], ["Watch", 1800, False]]],
@@ -39,4 +38,4 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/text_analysis/run.py

@@ -26,9 +26,9 @@ def text_analysis(text):
     return pos_tokens, pos_count, html
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     text_analysis,
-    gr.inputs.Textbox(placeholder="Enter sentence here..."),
+    gr.Textbox(placeholder="Enter sentence here..."),
     ["highlight", "key_values", "html"],
     examples=[
         ["What a beautiful morning for a walk!"],
@@ -36,6 +36,5 @@ iface = gr.Interface(
     ],
 )
 
-iface.test_launch()
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/titanic_survival/run.py

@@ -1,11 +1,7 @@
 import os
 
-import numpy as np
 import pandas as pd
-import sklearn
-from sklearn import preprocessing
 from sklearn.ensemble import RandomForestClassifier
-from sklearn.metrics import accuracy_score
 from sklearn.model_selection import train_test_split
 
 import gradio as gr
@@ -88,20 +84,20 @@ def predict_survival(passenger_class, is_male, age, company, fare, embark_point)
     df = encode_age(df)
     df = encode_fare(df)
     pred = clf.predict_proba(df)[0]
-    return {"Perishes": pred[0], "Survives": pred[1]}
+    return {"Perishes": float(pred[0]), "Survives": float(pred[1])}
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     predict_survival,
     [
-        gr.inputs.Dropdown(["first", "second", "third"], type="index"),
+        gr.Dropdown(["first", "second", "third"], type="index"),
         "checkbox",
-        gr.inputs.Slider(0, 80),
-        gr.inputs.CheckboxGroup(
+        gr.Slider(minimum=0, maximum=80),
+        gr.CheckboxGroup(
             ["Sibling", "Child"], label="Travelling with (select all)"
         ),
-        gr.inputs.Number(),
-        gr.inputs.Radio(["S", "C", "Q"], type="index"),
+        gr.Number(),
+        gr.Radio(["S", "C", "Q"], type="index"),
     ],
     "label",
     examples=[
@@ -113,4 +109,4 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/video_flip/run.py

@@ -5,7 +5,7 @@ def video_flip(video):
     return video
 
 
-iface = gr.Interface(video_flip, gr.inputs.Video(source="webcam"), "playable_video")
+demo = gr.Interface(video_flip, gr.Video(source="webcam"), "playable_video")
 
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/webcam/run.py

@@ -7,6 +7,7 @@ def snap(image):
     return np.flipud(image)
 
 
-iface = gr.Interface(snap, gr.inputs.Image(source="webcam", tool=None), "image")
+demo = gr.Interface(snap, gr.Image(source="webcam", tool=None), "image")
+
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/write_config.py

@@ -6,7 +6,7 @@ Example: python write_config.py calculator output.json
 Assumes:
 - The demo_name is a folder in this directory
 - The demo_name folder contains a run.py file 
-- The run.py which defines a Gradio Interface/Blocks instance called demo
+- The run.py file defines a Gradio Interface/Blocks instance called `demo`
 """
 
 from __future__ import annotations

demo/xray_blocks/run.py

@@ -1,4 +1,3 @@
-from textwrap import indent
 import gradio as gr
 
 import random
@@ -6,26 +5,26 @@ import random
 xray_model = lambda diseases, img: {disease: random.random() for disease in diseases}
 ct_model = lambda diseases, img: {disease: 0.1 for disease in diseases}
 
-xray_blocks = gr.Blocks()
+demo = gr.Blocks()
 
-with xray_blocks:
-    gr.components.Markdown(
-        """
-	# Detect Disease From Scan
-	With this model you can lorem ipsum
-	- ipsum 1
-	- ipsum 2
-	"""
+with demo:
+    gr.Markdown(
+"""
+# Detect Disease From Scan
+With this model you can lorem ipsum
+- ipsum 1
+- ipsum 2
+"""
     )
-    disease = gr.components.CheckboxGroup(
+    disease = gr.CheckboxGroup(
         choices=["Covid", "Malaria", "Lung Cancer"], label="Disease to Scan For"
     )
 
     with gr.Tabs():
         with gr.TabItem("X-ray"):
             with gr.Row():
-                xray_scan = gr.components.Image()
-                xray_results = gr.components.JSON()
+                xray_scan = gr.Image()
+                xray_results = gr.JSON()
             xray_run = gr.Button(
                 "Run", css={"background-color": "red", "--hover-color": "orange"}
             )
@@ -35,12 +34,12 @@ with xray_blocks:
 
         with gr.TabItem("CT Scan"):
             with gr.Row():
-                ct_scan = gr.components.Image()
-                ct_results = gr.components.JSON()
+                ct_scan = gr.Image()
+                ct_results = gr.JSON()
             ct_run = gr.Button("Run")
             ct_run.click(ct_model, inputs=[disease, ct_scan], outputs=ct_results)
 
-    overall_probability = gr.components.Textbox()
+    overall_probability = gr.Textbox()
 
-print(xray_blocks.get_config_file())
-xray_blocks.launch()
+if __name__ == "__main__":
+    demo.launch()

demo/zip_to_json/run.py

@@ -17,6 +17,7 @@ def zip_to_json(file_obj):
     return files
 
 
-iface = gr.Interface(zip_to_json, "file", "json")
+demo = gr.Interface(zip_to_json, "file", "json")
+
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/zip_two_files/run.py

@@ -10,7 +10,7 @@ def zip_two_files(file1, file2):
     return "tmp.zip"
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     zip_two_files,
     ["file", "file"],
     "file",
@@ -20,4 +20,4 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

gradio/interface.py

@@ -658,6 +658,10 @@ class Interface(Blocks):
         return interpretation.run_interpret(self, raw_input)
 
     def test_launch(self) -> None:
+        """
+        Passes a few samples through the function to test if the inputs/outputs
+        components are consistent with the function parameter and return values.
+        """
         for predict_fn in self.predict:
             print("Test launch: {}()...".format(predict_fn.__name__), end=" ")
             raw_input = []