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import sys
from pathlib import Path
file = Path(__file__).resolve()
parent, root = file.parent, file.parents[1]
sys.path.append(str(root))
import gradio
from fastapi import FastAPI, Request, Response
import random
import numpy as np
import pandas as pd
from titanic_model.processing.data_manager import load_dataset, load_pipeline
from titanic_model import __version__ as _version
from titanic_model.config.core import config
from sklearn.model_selection import train_test_split
from titanic_model.predict import make_prediction
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
# FastAPI object
app = FastAPI()
################################# Prometheus related code START ######################################################
import prometheus_client as prom
acc_metric = prom.Gauge('titanic_accuracy_score', 'Accuracy score for few random 100 test samples')
f1_metric = prom.Gauge('titanic_f1_score', 'F1 score for few random 100 test samples')
precision_metric = prom.Gauge('titanic_precision_score', 'Precision score for few random 100 test samples')
recall_metric = prom.Gauge('titanic_recall_score', 'Recall score for few random 100 test samples')
# LOAD TEST DATA
pipeline_file_name = f"{config.app_config.pipeline_save_file}{_version}.pkl"
titanic_pipe= load_pipeline(file_name=pipeline_file_name)
data = load_dataset(file_name=config.app_config.training_data_file) # read complete data
X_train, X_test, y_train, y_test = train_test_split( # divide into train and test set
data[config.model_config.features],
data[config.model_config.target],
test_size=config.model_config.test_size,
random_state=config.model_config.random_state,
)
test_data = X_test.copy()
test_data['target'] = y_test.values
# Function for updating metrics
def update_metrics():
global test_data
# Performance on test set
size = random.randint(100, 130)
test = test_data.sample(size, random_state = random.randint(0, 1e6)) # sample few 100 rows randomly
y_pred = titanic_pipe.predict(test.iloc[:, :-1]) # prediction
acc = accuracy_score(test['target'], y_pred).round(3) # accuracy score
f1 = f1_score(test['target'], y_pred).round(3) # F1 score
precision = precision_score(test['target'], y_pred).round(3) # Precision score
recall = recall_score(test['target'], y_pred).round(3) # Recall score
acc_metric.set(acc)
f1_metric.set(f1)
precision_metric.set(precision)
recall_metric.set(recall)
@app.get("/metrics")
async def get_metrics():
update_metrics()
return Response(media_type="text/plain", content= prom.generate_latest())
################################# Prometheus related code END ######################################################
# UI - Input components
in_Pid = gradio.Textbox(lines=1, placeholder=None, value="79", label='Passenger Id')
in_Pclass = gradio.Radio(['1', '2', '3'], type="value", label='Passenger class')
in_Pname = gradio.Textbox(lines=1, placeholder=None, value="Caldwell, Master. Alden Gates", label='Passenger Name')
in_sex = gradio.Radio(["Male", "Female"], type="value", label='Gender')
in_age = gradio.Textbox(lines=1, placeholder=None, value="14", label='Age of the passenger in yrs')
in_sibsp = gradio.Textbox(lines=1, placeholder=None, value="0", label='No. of siblings/spouse of the passenger aboard')
in_parch = gradio.Textbox(lines=1, placeholder=None, value="2", label='No. of parents/children of the passenger aboard')
in_ticket = gradio.Textbox(lines=1, placeholder=None, value="248738", label='Ticket number')
in_cabin = gradio.Textbox(lines=1, placeholder=None, value="A5", label='Cabin number')
in_embarked = gradio.Radio(["Southampton", "Cherbourg", "Queenstown"], type="value", label='Port of Embarkation')
in_fare = gradio.Textbox(lines=1, placeholder=None, value="29", label='Passenger fare')
# UI - Output component
out_label = gradio.Textbox(type="text", label='Prediction', elem_id="out_textbox")
# Label prediction function
def get_output_label(in_Pid, in_Pclass, in_Pname, in_sex, in_age, in_sibsp, in_parch, in_ticket, in_cabin, in_embarked, in_fare):
input_df = pd.DataFrame({"PassengerId": [in_Pid],
"Pclass": [int(in_Pclass)],
"Name": [in_Pname],
"Sex": [in_sex.lower()],
"Age": [float(in_age)],
"SibSp": [int(in_sibsp)],
"Parch": [int(in_parch)],
"Ticket": [in_ticket],
"Cabin": [in_cabin],
"Embarked": [in_embarked[0]],
"Fare": [float(in_fare)]})
result = make_prediction(input_data=input_df.replace({np.nan: None}))["predictions"]
label = "Survived" if result[0]==1 else "Not Survived"
return label
# Create Gradio interface object
iface = gradio.Interface(fn = get_output_label,
inputs = [in_Pid, in_Pclass, in_Pname, in_sex, in_age, in_sibsp, in_parch, in_ticket, in_cabin, in_embarked, in_fare],
outputs = [out_label],
title="Titanic Survival Prediction API ⛴",
description="Predictive model that answers the question: “What sort of people were more likely to survive?”",
allow_flagging='never',
)
# Mount gradio interface object on FastAPI app at endpoint = '/'
app = gradio.mount_gradio_app(app, iface, path="/")
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8001)
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