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import gradio as gr
def sample_func(inp):
pass
import numpy as np
import pandas as pd
import subprocess
import sys
subprocess.check_call([sys.executable,'-m','pip','install','tensorflow'])
subprocess.check_call([sys.executable,'-m','pip','install','scikit-learn'])
from sklearn.preprocessing import LabelEncoder, LabelBinarizer
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import KFold
from sklearn.ensemble import RandomForestClassifier
# Load data
df = pd.read_csv('ExperimentalMigraneData.csv')
X = df[['Age','Duration','Frequency','Location','Character','Intensity','Nausea','Vomit','Phonophobia','Photophobia','Visual','Sensory','Dysphasia','Dysarthria','Vertigo','Tinnitus','Hypoacusis','Diplopia','Visual_defect','Ataxia','Conscience','Paresthesia','DPF', 'On Periods']].values #selección de variables de entrada
Y = df['Types'] #select target
# Define the base Keras model
def baseline_model():
model = Sequential()
model.add(Dense(14, input_dim = 24, activation = 'relu')) # Rectified Linear Unit Activation Function
model.add(Dense(14, activation = 'relu'))
model.add(Dense(3, activation = 'softmax')) # Softmax for multi-class classification
model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
return model
# Define the Keras Classifier to use the base model
estimator = KerasClassifier(build_fn = baseline_model, epochs = 100, batch_size = 10, verbose = 0)
# Train the model
estimator.fit(X, Y)
# Define the input component with 24 number inputs
inputs = []
for i in range(24):
inputs.append(gr.inputs.Number(label=df.columns[i]))
# Define the output component to show the predicted output
output = gr.outputs.Label(label="Output")
# Define the migraine type mapping dictionary
migraine_types = {0: 'Menopause Stage',
1: 'Menstruation Stage',
2: 'Pre-Menopause Stage'}
# Define the Gradio interface function
def predict(*args):
# Convert the inputs into a numpy array
input_array = np.array(args).reshape(1, -1)
# Use the pre-trained estimator to predict the output based on the input array
y_pred = estimator.predict(input_array)
# Map the integer prediction to corresponding migraine type
predicted_type = migraine_types[int(y_pred[0])]
# Return the predicted output as text
return predicted_type
# Run the Gradio interface
interface = gr.Interface(fn=predict, inputs=inputs, outputs=output)
Home = gr.Interface(fn=sample_func, inputs=[gr.Image('Beige Classic Circular Fashion Fashion Animated Logo.png', label='ANTICIPATING QOL WITH MENOPAUSAL SEVERITY',shape=[40,40]),
gr.Textbox('PCL Project - Team: Technohommies', label='FYP', interactive=False).style(container=True),
gr.Textbox('ANTICIPATING MENSTRUAL MIGRAINE USING DEEP LEARNING', label='Project Title', interactive=False).style(container=True),
gr.Textbox("Pranav Polavarapu - 19BTRCR008 | Hrishikesh Reddy - 19BTRCR028 | Sai Keerthi Chelluri - 19BTRCR036 | Sai Sharanya Y - 19BTRCR043", label='TEAM', interactive=False).style(container=True),
gr.Textbox('Dr. S Vijaykumar', label='PCL Project Guide', interactive=False).style(container=True)], outputs=None, title="Project Centric Learnning", live=True)
Instructions = gr.Interface(fn=sample_func, inputs=[gr.Image('features-Input-Instructions.png', label='Instructions for User Inputs in the Testing Interface',shape=[60,60],interactive=False),
gr.Textbox("Please Proceed to the Next Tab - 'MENOPAUSAL QOL Model' for accessing the Model's Test Interface, & Provide the necessary inputs according to the instructions mentioned above", label='GO TO NEXT TAB/PAGE', interactive=False).style(container=True)] , outputs=None,
title="Instructions for User Inputs", live=True)
with gr.Blocks(css=".gradio-container {background-image: url('file=Beige Classic Circular Fashion Fashion Animated Logo.png')}") as demo:
gr.Markdown( """
## Welcome to the
# MENOPAUSAL QOL PREDICTOR
#### Please Give your inputs in the page below - as per the specified instructions
""")
with gr.Box():
with gr.Column():
with gr.Tab("MENOPAUSAL QOL PREDICTOR MODEL"):
with gr.Row(variant='panel'):
data = gr.TabbedInterface([Home, Instructions, interface], ["Home", "Guidelines", "MENOPAUSAL QOL PREDICTOR MODEL"])
demo.launch()
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