Upload 3 files

app.py

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+import streamlit as st
+import pandas as pd
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from sklearn.preprocessing import StandardScaler
+import numpy as np
+import pickle
+
+# Load the model
+model = load_model('my_model.h5')
+
+# Load the scaler
+with open('scaler.pkl', 'rb') as f:
+    scaler = pickle.load(f)
+
+# Create a sample input dataframe
+sample_data = {
+    'Application mode': [1],
+    'Application order': [1],
+    'Previous qualification (grade)': [125],
+    'Admission grade': [119],
+    'Displaced': [1],
+    'Debtor': [0],
+    'Tuition fees up to date': [1],
+    'Gender': [0],
+    'Scholarship holder': [0],
+    'Age at enrollment': [18],
+    'Curricular units 1st sem (enrolled)': [6],
+    'Curricular units 1st sem (evaluations)': [8],
+    'Curricular units 1st sem (approved)': [4],
+    'Curricular units 1st sem (grade)': [11],
+    'Curricular units 2nd sem (enrolled)': [6],
+    'Curricular units 2nd sem (evaluations)': [9],
+    'Curricular units 2nd sem (approved)': [0],
+    'Curricular units 2nd sem (grade)': [0],
+    'Curricular units 2nd sem (without evaluations)': [1]
+}
+
+sample_df = pd.DataFrame(sample_data)
+
+# Function to get user input
+def get_user_input():
+    input_data = {
+        'Application mode': 1,
+        'Application order': 1,
+        'Previous qualification (grade)': st.number_input('Previous qualification (grade)', value=int(sample_df['Previous qualification (grade)'][0])),
+        'Admission grade': st.number_input('Admission grade', value=int(sample_df['Admission grade'][0])),
+        'Displaced': st.selectbox('Displaced', options=[0, 1], format_func=lambda x: 'Yes' if x == 1 else 'No'),
+        'Debtor': st.selectbox('Debtor', options=[0, 1], format_func=lambda x: 'Yes' if x == 1 else 'No'),
+        'Tuition fees up to date': st.selectbox('Tuition fees up to date', options=[0, 1], format_func=lambda x: 'Yes' if x == 1 else 'No'),
+        'Gender': st.selectbox('Gender', options=[0, 1], format_func=lambda x: 'Female' if x == 1 else 'Male'),
+        'Scholarship holder': st.selectbox('Scholarship holder', options=[0, 1], format_func=lambda x: 'Yes' if x == 1 else 'No'),
+        'Age at enrollment': st.number_input('Age at enrollment', value=int(sample_df['Age at enrollment'][0])),
+        'Curricular units 1st sem (enrolled)': st.number_input('Curricular units 1st sem (enrolled)', value=int(sample_df['Curricular units 1st sem (enrolled)'][0])),
+        'Curricular units 1st sem (evaluations)': st.number_input('Curricular units 1st sem (evaluations)', value=int(sample_df['Curricular units 1st sem (evaluations)'][0])),
+        'Curricular units 1st sem (approved)': st.number_input('Curricular units 1st sem (approved)', value=int(sample_df['Curricular units 1st sem (approved)'][0])),
+        'Curricular units 1st sem (grade)': st.number_input('Curricular units 1st sem (grade)', value=int(sample_df['Curricular units 1st sem (grade)'][0])),
+        'Curricular units 2nd sem (enrolled)': st.number_input('Curricular units 2nd sem (enrolled)', value=int(sample_df['Curricular units 2nd sem (enrolled)'][0])),
+        'Curricular units 2nd sem (evaluations)': st.number_input('Curricular units 2nd sem (evaluations)', value=int(sample_df['Curricular units 2nd sem (evaluations)'][0])),
+        'Curricular units 2nd sem (approved)': st.number_input('Curricular units 2nd sem (approved)', value=int(sample_df['Curricular units 2nd sem (approved)'][0])),
+        'Curricular units 2nd sem (grade)': st.number_input('Curricular units 2nd sem (grade)', value=int(sample_df['Curricular units 2nd sem (grade)'][0])),
+        'Curricular units 2nd sem (without evaluations)': st.number_input('Curricular units 2nd sem (without evaluations)', value=int(sample_df['Curricular units 2nd sem (without evaluations)'][0]))
+    }
+    return pd.DataFrame(input_data, index=[0])
+
+# Streamlit app
+st.title('Student Outcome Prediction 🧑‍🎓')
+st.write('This app predicts whether a student is enrolled, graduated or dropout. 🎓')
+st.write('Model is trained on a dataset by UC Irvine, acquired from several disjoint higher education institution databases. It has 82% accuracy. 🏫')
+
+# Get user input
+user_input_df = get_user_input()
+
+# Normalize the user input
+user_input_scaled = scaler.transform(user_input_df)
+
+# Make prediction
+prediction = model.predict(user_input_scaled)
+predicted_class = prediction.argmax(axis=1)[0]
+
+# Map the predicted class back to words
+target_mapping = {0: 'Graduate', 1: 'Dropout', 2: 'Enrolled'}
+predicted_label = target_mapping[predicted_class]
+
+# Display the prediction
+st.header(f'The predicted outcome is: ***{predicted_label}***')
+st.image('https://d13b2ieg84qqce.cloudfront.net/c42fed0bb5d4f458b8606152e7dec885cd3e751d')

my_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5675cc27256fa60ad8c4ce225a4175881d52612753e1a5e2ed5e97d9eb3acf73
+size 9290248

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9a5f8f60e304a736508c033168586b624754c523a0d186900e3161bf09accc08
+size 1532