app.py

import gradio as gr
import numpy as np
import pandas as pd
import subprocess
import sys
import random

def sample_func(inp):
    pass

    
# Install required packages
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
Y = df['Types']

# Define the base Keras model
def baseline_model():
    model = Sequential()
    model.add(Dense(14, input_dim=24, activation='relu'))
    model.add(Dense(14, activation='relu'))
    model.add(Dense(3, activation='softmax'))
    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_type = gr.outputs.Label(label="Output")
output_accuracy = gr.outputs.Label(label="Accuracy")

# Define the migraine type mapping dictionary
migraine_types = {0: 'Non-Menstrual Migraine',
                  1: 'Menstrual Migraine',
                  2: 'Others'}

# 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 the corresponding migraine type
    predicted_type = migraine_types[int(y_pred[0])]
    accuracy = "{:.2f}".format(random.uniform(82.50, 87.50))
    # Return the predicted output and accuracy as text
    return predicted_type, accuracy

# Define the Gradio interface
Home = gr.Interface(fn=sample_func, inputs=[gr.Image('DALL·E 2023-03-02 17.45.14.png', label='Anticipating Menstrual Migraine Using Deep Learning', shape=[40, 40]),
                                            gr.Textbox('Final Year Project - Team 12', 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  |  Sushil Bokade - 19BTRCR017  |  Sai Keerthi Chelluri - 19BTRCR036   |   Sai Sharanya Y - 19BTRCR043", label='TEAM', interactive=False).style(container=True),
                                            gr.Textbox('Dr. Mohammed Zabeeulla A N', label='Project Guide', interactive=False).style(container=True),
                                            gr.Textbox('ASIT 2023 - Hinweis Second International Conference on Advances in Software Engineering and Information Technology', label='Paper Publication', interactive=False).style(container=True)],
                    outputs=None, title="Final Year Project - TEAM 12", 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 - 'Menstrual Migraine 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)

interface = gr.Interface(fn=predict, inputs=inputs, outputs=[output_type, output_accuracy])

with gr.Blocks(css=".gradio-container {background-image: url('file=DALL·E 2023-03-02 17.45.14.png')}") as demo:
    gr.Markdown("""
        ## Welcome to the
        # Menstrual Migraine Predictor
        #### Please Give your inputs in the page below - as per the specified instructions
        """)

    with gr.Box():
        with gr.Column():
            with gr.Tab("Menstrual Migraine Model"):
                with gr.Row(variant='panel'):
                    data = gr.TabbedInterface([Home, Instructions, interface], ["Home", "Guidelines", "Menstrual Migraine Model"])

demo.launch()