!pip list | grep gradiogradio 4.27.0
gradio_client 0.15.1Gradio
Gradio
Basic Interface: text, slider -> text
import gradio as gr
def greet(name, intensity):
return "Hello, " + name + "!" * int(intensity)
demo = gr.Interface(
fn=greet,
inputs=["text", "slider"],
outputs=["text"],
)
demo.launch()Running on local URL: http://127.0.0.1:7860
To create a public link, set `share=True` in `launch()`.gr.Interface(
fn: 'Callable',
inputs: 'str | Component | list[str | Component] | None',
outputs: 'str | Component | list[str | Component] | None',
examples: 'list[Any] | list[list[Any]] | str | None' = None,
cache_examples: "bool | Literal['lazy'] | None" = None,
examples_per_page: 'int' = 10,
live: 'bool' = False,
title: 'str | None' = None,
description: 'str | None' = None,
article: 'str | None' = None,
thumbnail: 'str | None' = None,
theme: 'Theme | str | None' = None,
css: 'str | None' = None,
allow_flagging: "Literal['never'] | Literal['auto'] | Literal['manual'] | None" = None,
flagging_options: 'list[str] | list[tuple[str, str]] | None' = None,
flagging_dir: 'str' = 'flagged',
flagging_callback: 'FlaggingCallback | None' = None,
analytics_enabled: 'bool | None' = None,
batch: 'bool' = False,
max_batch_size: 'int' = 4,
api_name: 'str | Literal[False] | None' = 'predict',
_api_mode: 'bool' = False,
allow_duplication: 'bool' = False,
concurrency_limit: "int | None | Literal['default']" = 'default',
js: 'str | None' = None,
head: 'str | None' = None,
additional_inputs: 'str | Component | list[str | Component] | None' = None,
additional_inputs_accordion: 'str | Accordion | None' = None,
*,
submit_btn: 'str | Button' = 'Submit',
stop_btn: 'str | Button' = 'Stop',
clear_btn: 'str | Button' = 'Clear',
delete_cache: 'tuple[int, int] | None' = None,
**kwargs,
)Image output: slider -> image
import gradio as gr
import numpy as np
import time
def fake_diffusion(steps):
rng = np.random.default_rng()
for i in range(steps):
time.sleep(1)
image = rng.random(size=(600, 600, 3))
yield image
image = np.ones((1000,1000,3), np.uint8)
image[:] = [255, 124, 0]
yield image
demo = gr.Interface(fake_diffusion,
inputs=gr.Slider(1, 10, 3, step=1),
outputs="image")
demo.launch()Running on local URL: http://127.0.0.1:7861
To create a public link, set `share=True` in `launch()`.Progress bar: text -> text
import gradio as gr
import time
def slowly_reverse(word, progress=gr.Progress()):
progress(0, desc="Starting")
time.sleep(1)
progress(0.05)
new_string = ""
for letter in progress.tqdm(word, desc="Reversing"):
time.sleep(0.25)
new_string = letter + new_string
return new_string
demo = gr.Interface(slowly_reverse, gr.Text(), gr.Text())
demo.launch()Running on local URL: http://127.0.0.1:7862
To create a public link, set `share=True` in `launch()`.import gradio as gr
secret_word = "gradio"
with gr.Blocks() as demo:
used_letters_var = gr.State([])
with gr.Row() as row:
with gr.Column():
input_letter = gr.Textbox(label="Enter letter")
btn = gr.Button("Guess Letter")
with gr.Column():
hangman = gr.Textbox(
label="Hangman",
value="_"*len(secret_word)
)
used_letters_box = gr.Textbox(label="Used Letters")
def guess_letter(letter, used_letters):
used_letters.append(letter)
answer = "".join([
(letter if letter in used_letters else "_")
for letter in secret_word
])
return {
used_letters_var: used_letters,
used_letters_box: ", ".join(used_letters),
hangman: answer
}
btn.click(
guess_letter,
[input_letter, used_letters_var],
[used_letters_var, used_letters_box, hangman]
)
demo.launch()Running on local URL: http://127.0.0.1:7863
To create a public link, set `share=True` in `launch()`.Audio: audio -> audio
import gradio as gr
from pydub import AudioSegment
from time import sleep
with gr.Blocks() as demo:
input_audio = gr.Audio(label="Input Audio", type="filepath", format="mp3")
with gr.Row():
with gr.Column():
stream_as_file_btn = gr.Button("Stream as File")
format = gr.Radio(["wav", "mp3"], value="wav", label="Format")
stream_as_file_output = gr.Audio(streaming=True)
def stream_file(audio_file, format):
audio = AudioSegment.from_file(audio_file)
i = 0
chunk_size = 1000
while chunk_size * i < len(audio):
chunk = audio[chunk_size * i : chunk_size * (i + 1)]
i += 1
if chunk:
file = f"/tmp/{i}.{format}"
chunk.export(file, format=format)
yield file
sleep(0.5)
stream_as_file_btn.click(
stream_file, [input_audio, format], stream_as_file_output
)
gr.Examples(
[["audio/cantina.wav", "wav"], ["audio/cantina.wav", "mp3"]],
[input_audio, format],
fn=stream_file,
outputs=stream_as_file_output,
)
with gr.Column():
stream_as_bytes_btn = gr.Button("Stream as Bytes")
stream_as_bytes_output = gr.Audio(format="bytes", streaming=True)
def stream_bytes(audio_file):
chunk_size = 20_000
with open(audio_file, "rb") as f:
while True:
chunk = f.read(chunk_size)
if chunk:
yield chunk
sleep(1)
else:
break
stream_as_bytes_btn.click(stream_bytes, input_audio, stream_as_bytes_output)
if __name__ == "__main__":
demo.queue().launch()Running on local URL: http://127.0.0.1:7865
To create a public link, set `share=True` in `launch()`.Traceback (most recent call last):
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/gradio/queueing.py", line 527, in process_events
response = await route_utils.call_process_api(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/gradio/route_utils.py", line 261, in call_process_api
output = await app.get_blocks().process_api(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/gradio/blocks.py", line 1784, in process_api
inputs = await self.preprocess_data(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/gradio/blocks.py", line 1506, in preprocess_data
processed_input.append(block.preprocess(inputs_cached))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/gradio/components/audio.py", line 231, in preprocess
processing_utils.audio_to_file(
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/gradio/processing_utils.py", line 560, in audio_to_file
file = audio.export(filename, format=format)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/pydub/audio_segment.py", line 970, in export
raise CouldntEncodeError(
pydub.exceptions.CouldntEncodeError: Encoding failed. ffmpeg/avlib returned error code: 127
Command:['ffmpeg', '-y', '-f', 'wav', '-i', '/tmp/tmplaiefr8j', '-f', 'mp3', '/tmp/tmpiojnrpsx']
Output from ffmpeg/avlib:
ffmpeg: error while loading shared libraries: libopenh264.so.5: cannot open shared object file: No such file or directory
Traceback (most recent call last):
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/gradio/queueing.py", line 527, in process_events
response = await route_utils.call_process_api(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/gradio/route_utils.py", line 261, in call_process_api
output = await app.get_blocks().process_api(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/gradio/blocks.py", line 1784, in process_api
inputs = await self.preprocess_data(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/gradio/blocks.py", line 1506, in preprocess_data
processed_input.append(block.preprocess(inputs_cached))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/gradio/components/audio.py", line 231, in preprocess
processing_utils.audio_to_file(
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/gradio/processing_utils.py", line 560, in audio_to_file
file = audio.export(filename, format=format)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/pydub/audio_segment.py", line 970, in export
raise CouldntEncodeError(
pydub.exceptions.CouldntEncodeError: Encoding failed. ffmpeg/avlib returned error code: 127
Command:['ffmpeg', '-y', '-f', 'wav', '-i', '/tmp/tmpuqq4b20d', '-f', 'mp3', '/tmp/tmphx041ptn']
Output from ffmpeg/avlib:
ffmpeg: error while loading shared libraries: libopenh264.so.5: cannot open shared object file: No such file or directory
import torch
model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()Downloading: "https://github.com/pytorch/vision/zipball/v0.6.0" to /home/ben/.cache/torch/hub/v0.6.0.zip
/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
warnings.warn(
/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)import requests
from PIL import Image
from torchvision import transforms
# Download human-readable labels for ImageNet.
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")
def predict(inp):
inp = transforms.ToTensor()(inp).unsqueeze(0)
with torch.no_grad():
prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
return confidencesimport gradio as gr
gr.Interface(fn=predict,
inputs=gr.Image(type="pil"),
outputs=gr.Label(num_top_classes=3),
examples=["tiger.jpg", "cheetah.jpg"]).launch()Running on local URL: http://127.0.0.1:7869
Thanks for being a Gradio user! If you have questions or feedback, please join our Discord server and chat with us: https://discord.gg/feTf9x3ZSB
To create a public link, set `share=True` in `launch()`.Traceback (most recent call last):
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/gradio/queueing.py", line 527, in process_events
response = await route_utils.call_process_api(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/gradio/route_utils.py", line 261, in call_process_api
output = await app.get_blocks().process_api(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/gradio/blocks.py", line 1788, in process_api
result = await self.call_function(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/gradio/blocks.py", line 1340, in call_function
prediction = await anyio.to_thread.run_sync(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/anyio/to_thread.py", line 33, in run_sync
return await get_asynclib().run_sync_in_worker_thread(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/anyio/_backends/_asyncio.py", line 877, in run_sync_in_worker_thread
return await future
^^^^^^^^^^^^
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/anyio/_backends/_asyncio.py", line 807, in run
result = context.run(func, *args)
^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/gradio/utils.py", line 759, in wrapper
response = f(*args, **kwargs)
^^^^^^^^^^^^^^^^^^
File "/tmp/ipykernel_145374/855710716.py", line 10, in predict
inp = transforms.ToTensor()(inp).unsqueeze(0)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/torchvision/transforms/transforms.py", line 137, in __call__
return F.to_tensor(pic)
^^^^^^^^^^^^^^^^
File "/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/torchvision/transforms/functional.py", line 140, in to_tensor
raise TypeError(f"pic should be PIL Image or ndarray. Got {type(pic)}")
TypeError: pic should be PIL Image or ndarray. Got <class 'NoneType'>