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app/main.py

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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)