File size: 4,910 Bytes
a34aae9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 |
import gradio as gr
from SVM.SVM_C import SVM_Classifier
from SVM.SVM_R import SVM_Regressor
class SVM_UI():
def __init__(self):
#self.svm = SVM_Classifier()
self.svm = SVM_Regressor()
def predict(self, weight, height, gender, fat, freq, experience, duration, workout):
# Update the SVM model with current values
self.svm.weight = weight
self.svm.height = height
self.svm.gender = gender
self.svm.fat = fat
self.svm.freq = freq
self.svm.experience = experience
self.svm.duration = duration
self.svm.workout = workout
# Get prediction and debug info
prediction = self.svm.make_prediction()
debug_info = self.svm.get_debug_info()
return prediction, debug_info
def get_interface(self) -> gr.Blocks:
result_text = gr.Textbox(label="Modify the settings to check how much water should you take:",interactive=False)
debug_text = gr.Textbox(label="Debug Information", interactive=False, lines=20, info="This is for the one who made this site... do not worry about this ;)") # Make it bigger for debug info
with gr.Blocks() as interface:
gr.Markdown("hello world!")
gr.Markdown("In this model we are using a Support Vector Machine tuned for a regression target.")
gr.Markdown("Based on the information you provide, the model will recommend the amount of water (in litres) you need to consume for your intended workout session.")
inputs = []
with gr.Column():
gr.HTML("<hr style='border: 1px solid #ccc; width: 100%;'>")
with gr.Column():
gr.Markdown("# Water consumption for workout session")
gr.Markdown("How much water should you take with you for your next workout session:")
result_text.render()
with gr.Row():
with gr.Column():# as network_parameters_ui:
gr.Markdown("# About you")
slider1 = gr.Slider(40, 140, value=self.svm.weight, step=1.0, label="Weight (kg)", interactive=True)
inputs.append(slider1)
slider2 = gr.Slider(1.5, 2.2, value=self.svm.height, step=0.01, label="Height (m)", interactive=True)
inputs.append(slider2)
radio1 = gr.Radio(["Male", "Female"], value=self.svm.gender, label="Gender", interactive=True)
inputs.append(radio1)
with gr.Column():# as network_parameters_ui:
gr.Markdown("# Your fitness level")
slider4 = gr.Slider(20, 70, value=self.svm.fat, step=1.0, label="Fat percentage (%)", info="What is the percentage fat in your body?",interactive=True)
inputs.append(slider4)
slider5 = gr.Slider(1, 5, value=self.svm.freq, step=1.0, label="Workouts a week", info="How many times a week do you go to the gym?",interactive=True)
inputs.append(slider5)
slider6 = gr.Slider(1, 3, value=self.svm.experience, step=1.0, label="Experience level", info="1 = Beginner; 3 = Very experienced?",interactive=True)
inputs.append(slider6)
with gr.Column():
gr.Markdown("# Your session today")
slider3 = gr.Slider(0.1, 2.0, value=self.svm.duration, step=0.1, label="Session duration (hours)", info="How long are you planning to exercise today?",interactive=True)
inputs.append(slider3)
radio2 = gr.Radio(["Cardio", "HIIT", "Strength","Yoga"], value=self.svm.workout, label="Workout type", info="What are you planning to do today?", interactive=True)
inputs.append(radio2)
with gr.Column():
gr.HTML("<hr style='border: 1px solid #ccc; width: 100%;'>")
with gr.Column():
debug_text.render()
#predict_btn = gr.Button("Get Water recommendation", variant="primary")
#result_text = gr.Textbox(label="Recommendation",interactive=False)
#debug_text = gr.Textbox(label="Debug Information", interactive=False, lines=20) # Make it bigger for debug info
#predict_btn.click(fn=self.predict, inputs=inputs, outputs=[result_text, debug_text])
# Add debounced change event handlers to all inputs
for input_component in inputs:
input_component.change(
fn=self.predict,
inputs=inputs,
outputs=[result_text, debug_text]
)
return interface
|