Deploying Machine Learning model using Streamlit

Notebook එකක ඉන්න model එකකින් ඔයාට හැරෙන්නට කාටවත් වැඩක් නෑ. Python install කරලාවත් නැති සැබෑ කෙනෙකුට browser එකේ ඇත්තටම පාවිච්චි කරන්න පුළුවන් දෙයක් බවට හරවන step එකයි මේක.

A model sitting in a notebook helps nobody but you. This is the step that turns it into something a real person — with no Python installed — can actually use in a browser.

18 May 20265 min readSelf Guide · Stage 17#Shipping Models to the World#Deploy ML Model using Streamlit#Machine learning

Lesson 53 of 64 · Part 5 · Shipping Models to the World · Deploy ML Model using Streamlit · bilingual notes (English + සිංහල)

Why this matters. A model sitting in a notebook helps nobody but you. This is the step that turns it into something a real person — with no Python installed — can actually use in a browser.

සිංහලෙන්: Notebook එකක ඉන්න model එකකින් ඔයාට හැරෙන්නට කාටවත් වැඩක් නෑ. Python install කරලාවත් නැති සැබෑ කෙනෙකුට browser එකේ ඇත්තටම පාවිච්චි කරන්න පුළුවන් දෙයක් බවට හරවන step එකයි මේක.

What you'll use this for. Every prediction app you've ever used online started exactly here — a trained model wrapped in a simple form for input and output.

සිංහලෙන්: ඔයා කවදාහරි online පාවිච්චි කරපු prediction app ඕනම එකක් පටන් ගත්තෙ හරියටම මෙතනින් — input එකට, output එකට simple form එකකින් wrap කරපු train කරපු model එකක්.

Key principles

A deployed model needs a saved copy of the exact training pipeline, not just the model. If you standardized features before training, the app must standardize new input the exact same way — saving the scaler alongside the model, not just the model alone, is what this lesson teaches.
Deploy කරපු model එකකට ඕන model එක විතරක් නෙවෙයි, ඇත්තටම train කරපු pipeline එකේම save කරපු copy එකක්

Walkthrough

Importing the Dependencies

ExamplePython

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import svm
from sklearn.metrics import accuracy_score

Data Collection and Analysis

PIMA Diabetes Dataset

ExamplePython

# loading the diabetes dataset to a pandas DataFrame
diabetes_dataset = pd.read_csv('/content/diabetes.csv')

ExamplePython

# printing the first 5 rows of the dataset
diabetes_dataset.head()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	…
0	6	148	72	35	0	33.6	0.627	…
1	1	85	66	29	0	26.6	0.351	…
2	8	183	64	0	0	23.3	0.672	…
3	1	89	66	23	94	28.1	0.167	…
4	0	137	40	35	168	43.1	2.288	…

ExamplePython

# number of rows and Columns in this dataset
diabetes_dataset.shape

Output

Output

(768, 9)

ExamplePython

# getting the statistical measures of the data
diabetes_dataset.describe()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	…
count	768.000000	768.000000	768.000000	768.000000	768.000000	768.000000	768.000000	…
mean	3.845052	120.894531	69.105469	20.536458	79.799479	31.992578	0.471876	…
std	3.369578	31.972618	19.355807	15.952218	115.244002	7.884160	0.331329	…
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.078000	…
25%	1.000000	99.000000	62.000000	0.000000	0.000000	27.300000	0.243750	…
50%	3.000000	117.000000	72.000000	23.000000	30.500000	32.000000	0.372500	…

… 2 more rows

ExamplePython

diabetes_dataset['Outcome'].value_counts()

Output

Output

0    500
1    268
Name: Outcome, dtype: int64

0 --> Non-Diabetic

1 --> Diabetic

ExamplePython

diabetes_dataset.groupby('Outcome').mean()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age
Outcome
0	3.298000	109.980000	68.184000	19.664000	68.792000	30.304200	0.429734	31.190000
1	4.865672	141.257463	70.824627	22.164179	100.335821	35.142537	0.550500	37.067164

ExamplePython

# separating the data and labels
X = diabetes_dataset.drop(columns = 'Outcome', axis=1)
Y = diabetes_dataset['Outcome']

ExamplePython

print(X)

Output

Output

     Pregnancies  Glucose  BloodPressure  ...   BMI  DiabetesPedigreeFunction  Age
0              6      148             72  ...  33.6                     0.627   50
1              1       85             66  ...  26.6                     0.351   31
2              8      183             64  ...  23.3                     0.672   32
3              1       89             66  ...  28.1                     0.167   21
4              0      137             40  ...  43.1                     2.288   33
..           ...      ...            ...  ...   ...                       ...  ...
763           10      101             76  ...  32.9                     0.171   63
764            2      122             70  ...  36.8                     0.340   27
765            5      121             72  ...  26.2                     0.245   30
766            1      126             60  ...  30.1                     0.349   47
767            1       93             70  ...  30.4                     0.315   23

[768 rows x 8 columns]

ExamplePython

print(Y)

Output

Output

0      1
1      0
2      1
3      0
4      1
      ..
763    0
764    0
765    0
766    1
767    0
Name: Outcome, Length: 768, dtype: int64

Train Test Split

ExamplePython

X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.2, stratify=Y, random_state=2)

ExamplePython

print(X.shape, X_train.shape, X_test.shape)

Output

Output

(768, 8) (614, 8) (154, 8)

Training the Model

ExamplePython

classifier = svm.SVC(kernel='linear')

ExamplePython

#training the support vector Machine Classifier
classifier.fit(X_train, Y_train)

Output

Output

SVC(kernel='linear')

Model Evaluation

Accuracy Score

ExamplePython

# accuracy score on the training data
X_train_prediction = classifier.predict(X_train)
training_data_accuracy = accuracy_score(X_train_prediction, Y_train)

ExamplePython

print('Accuracy score of the training data : ', training_data_accuracy)

Output

Output

Accuracy score of the training data :  0.7833876221498371

ExamplePython

# accuracy score on the test data
X_test_prediction = classifier.predict(X_test)
test_data_accuracy = accuracy_score(X_test_prediction, Y_test)

ExamplePython

print('Accuracy score of the test data : ', test_data_accuracy)

Output

Output

Accuracy score of the test data :  0.7727272727272727

Making a Predictive System

ExamplePython

input_data = (5,166,72,19,175,25.8,0.587,51)

# changing the input_data to numpy array
input_data_as_numpy_array = np.asarray(input_data)

# reshape the array as we are predicting for one instance
input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)

prediction = classifier.predict(input_data_reshaped)
print(prediction)

if (prediction[0] == 0):
  print('The person is not diabetic')
else:
  print('The person is diabetic')

Output

Output

[1]
The person is diabetic

Output

Output

/usr/local/lib/python3.7/dist-packages/sklearn/base.py:446: UserWarning: X does not have valid feature names, but SVC was fitted with feature names
  "X does not have valid feature names, but"

Saving the trained model

ExamplePython

import pickle

ExamplePython

filename = 'trained_model.sav'
pickle.dump(classifier, open(filename, 'wb'))

ExamplePython

# loading the saved model
loaded_model = pickle.load(open('trained_model.sav', 'rb'))

ExamplePython

input_data = (5,166,72,19,175,25.8,0.587,51)

# changing the input_data to numpy array
input_data_as_numpy_array = np.asarray(input_data)

# reshape the array as we are predicting for one instance
input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)

prediction = loaded_model.predict(input_data_reshaped)
print(prediction)

if (prediction[0] == 0):
  print('The person is not diabetic')
else:
  print('The person is diabetic')

Output

Output

[1]
The person is diabetic

Output

Output

/usr/local/lib/python3.7/dist-packages/sklearn/base.py:446: UserWarning: X does not have valid feature names, but SVC was fitted with feature names
  "X does not have valid feature names, but"

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