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| from fastapi import FastAPI, Request, Response | |
| import gradio | |
| import joblib | |
| from xgboost import XGBClassifier | |
| import pandas as pd | |
| import numpy as np | |
| from sklearn.metrics import f1_score, precision_score, recall_score | |
| import prometheus_client as prom | |
| app = FastAPI() | |
| username = "runaksh" | |
| repo_name = "Patientsurvival-model" | |
| repo_path = username+ '/' + repo_name | |
| xgb_model_loaded = joblib.load("xgboost-model.pkl") | |
| import pandas as pd | |
| test_data = pd.read_csv("test_data.csv") | |
| f1_metric = prom.Gauge('sentiment_f1_score', 'F1 score for random 100 test samples') | |
| precision_metric = prom.Gauge('sentiment_precision_score', 'Precision score for random 100 test samples') | |
| recall_metric = prom.Gauge('sentiment_recall_score', 'Recall score for random 100 test samples') | |
| # Function for response generation | |
| # Load your trained model | |
| def bol_to_int(bol): | |
| if bol==True: | |
| return 1 | |
| else: | |
| return 0 | |
| # Function for prediction | |
| def predict_death_event(feature1, feature2, feature3,feature4, feature5, feature6, feature7, feature8, feature9, feature10, feature11, feature12): | |
| data = {'age' : [feature1], 'anaemia' : [bol_to_int(feature2)], | |
| 'creatinine_phosphokinase' : [feature3], | |
| 'diabetes' : [bol_to_int(feature4)], | |
| 'ejection_fraction' : [feature5], | |
| 'high_blood_pressure' : [bol_to_int(feature6)], | |
| 'platelets' : [feature7], | |
| 'serum_creatinine' : [feature8], | |
| 'serum_sodium' : [feature9], | |
| 'sex' : [bol_to_int(feature10)], | |
| 'smoking' : [bol_to_int(feature11)], | |
| 'time' : [feature12]} | |
| df = pd.DataFrame(data) | |
| y_pred = xgb_model_loaded.predict(df)[0] | |
| return y_pred | |
| # Function for updating metrics | |
| def update_metrics(): | |
| test = test_data.sample(100) | |
| test_text = test['Text'].values | |
| test_pred = sentiment_model(list(test_text)) | |
| pred_labels = [int(pred['label'].split("_")[1]) for pred in test_pred] | |
| f1 = f1_score(test['labels'], pred_labels).round(3) | |
| precision = precision_score(test['labels'], pred_labels).round(3) | |
| recall = recall_score(test['labels'], pred_labels).round(3) | |
| f1_metric.set(f1) | |
| precision_metric.set(precision) | |
| recall_metric.set(recall) | |
| async def get_metrics(): | |
| update_metrics() | |
| return Response(media_type="text/plain", content= prom.generate_latest()) | |
| # Gradio interface to generate UI link | |
| title = "Patient Survival Prediction" | |
| description = "Predict survival of patient with heart failure, given their clinical record" | |
| iface = gradio.Interface(fn = predict_death_event, | |
| inputs=[ | |
| gradio.components.Slider(30, 100, step=1, label= 'age'), | |
| gradio.components.Radio(["0","1"], label= 'anaemia'), | |
| gradio.components.Slider(1, 10000, step=1, label= 'creatinine_phosphokinase'), | |
| gradio.components.Radio(["0","1"], label= 'diabetes'), | |
| gradio.components.Slider(1, 100, step=1, label= 'ejection_fraction'), | |
| gradio.components.Radio(["0","1"], label= 'high_blood_pressure'), | |
| gradio.components.Number(label= 'platelets'), | |
| gradio.components.Slider(0.1, 10.0, step=0.1, label= 'serum_creatinine'), | |
| gradio.components.Slider(100, 150, step=1, label= 'serum_sodium'), | |
| gradio.components.Radio(["0","1"], label= 'sex'), | |
| gradio.components.Radio(["0","1"], label= 'smoking'), | |
| gradio.components.Slider(1, 300, step=1, label= 'time')], | |
| outputs = [gradio.components.Textbox (label ='DeathEvent')], | |
| title = title, | |
| description = description) | |
| app = gradio.mount_gradio_app(app, iface, path="/") | |
| #iface.launch(server_name = "0.0.0.0", server_port = 8001) # Ref. for parameters: https://www.gradio.app/docs/interface | |
| if __name__ == "__main__": | |
| # Use this for debugging purposes only | |
| import uvicorn | |
| uvicorn.run(app, host="0.0.0.0", port=8001) |