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import gradio as gr
import sys
import matplotlib
import matplotlib.pyplot as plt
from pathlib import Path
import torch
from torch import nn
import numpy as np
import PIL
sys.path.insert(0, "../resources")
from resources.module import GradioModule, register
@GradioModule
class Pictionary:
def __init__(self, txt, model) -> None:
self.LABELS = Path(txt).read_text().splitlines()
print(txt, model)
self.model = nn.Sequential(
nn.Conv2d(1, 32, 3, padding='same'),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(32, 64, 3, padding='same'),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(64, 128, 3, padding='same'),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(1152, 256),
nn.ReLU(),
nn.Linear(256, len(self.LABELS)),
)
state_dict = torch.load(model, map_location='cpu')
self.model.load_state_dict(state_dict, strict=False)
self.model.eval()
@register(inputs="sketchpad", outputs=gr.Label(), examples=None, live=True)
def perdict(self, img) -> 'dict[str, float]':
if type(img) == type(None): return {}
x = torch.tensor(img, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255.
with torch.no_grad():
out = self.model(x)
probabilities = torch.nn.functional.softmax(out[0], dim=0)
values, indices = torch.topk(probabilities, 5)
confidences = {self.LABELS[i]: v.item() for i, v in zip(indices, values)}
return confidences
@GradioModule
class HelloWorld_2_0:
@register(inputs=["text", "text", gr.Radio(["morning", "evening", "night"])], outputs="text")
def Hello(self, Lname : str, Fname : str, day : 'list[any]'=["morning", "evening", "night"]) -> str:
return "Hello, {} {}".format(Fname, Lname)
@register(inputs=["text", "text"], outputs="text")
def goodbye(self, Fname : str, Lname : str) -> str:
return "Goodbye, {} {}".format(Fname, Lname)
@register(inputs=["text", gr.Checkbox() , gr.Slider(0, 60)], outputs=["text", "number"])
def greet(self, name, is_morning, temperature):
salutation = "Good morning" if is_morning else "Good evening"
greeting = "%s %s. It is %s degrees today" % (salutation, name, temperature)
celsius = (temperature - 32) * 5 / 9
return (greeting, round(celsius, 2))
@GradioModule
class FSD:
def get_new_val(self, old_val, nc):
return np.round(old_val * (nc - 1)) / (nc - 1)
def palette_reduce(self, img : PIL.Image.Image, nc):
pixels = np.array(img, dtype=float) / 255
pixels = self.get_new_val(pixels, nc)
carr = np.array(pixels / np.max(pixels) * 255, dtype=np.uint8)
return PIL.Image.fromarray(carr)
@register(inputs=[gr.Image(), gr.Slider(0.00, 16)], outputs=gr.Gallery())
def Floyd_Steinberg_dithering(self, img, nc ):
pixels = np.array(img, dtype=float) / 255
new_height, new_width, _ = img.shape
for row in range(new_height):
for col in range(new_width):
old_val = pixels[row, col].copy()
new_val = self.get_new_val(old_val, nc)
pixels[row, col] = new_val
err = old_val - new_val
if col < new_width - 1:
pixels[row, col + 1] += err * 7 / 16
if row < new_height - 1:
if col > 0:
pixels[row + 1, col - 1] += err * 3 / 16
pixels[row + 1, col] += err * 5 / 16
if col < new_width - 1:
pixels[row + 1, col + 1] += err * 1 / 16
carr = np.array(pixels / np.max(pixels, axis=(0, 1)) * 255, dtype=np.uint8)
return [PIL.Image.fromarray(carr), self.palette_reduce(img, nc) ]
@GradioModule
class stock_forecast:
def __init__(self):
matplotlib.use('Agg')
@register(inputs=[gr.Checkbox(label="legend"), gr.Radio([2025, 2030, 2035, 2040], label="projct"), gr.CheckboxGroup(["Google", "Microsoft", "Gradio"], label="company"), gr.Slider(label="noise"), gr.Radio(["cross", "line", "circle"], label="style")], outputs=[gr.Plot()])
def plot_forcast(self, legend, project, companies , noise , styles)-> matplotlib.figure.Figure:
start_year = 2022
x = np.arange(start_year, project + 1)
year_count = x.shape[0]
plt_format = ({"cross": "X", "line": "-", "circle": "o--"})[styles]
fig = plt.figure()
ax = fig.add_subplot(111)
for i, company in enumerate(companies):
series = np.arange(0, year_count, dtype=float)
series = series**2 * (i + 1)
series += np.random.rand(year_count) * noise
ax.plot(x, series, plt_format)
if legend:
plt.legend(companies)
print(type(fig))
return fig
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