Create app.py

app.py

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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="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 - '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()
+