Update app.py

app.py

@@ -1,32 +1,12 @@
-from fastapi import FastAPI, Request, Response
-
+# Import the libraries
 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
+xgb_model_loaded = joblib.load("xgboost-model.pkl")
 
 def bol_to_int(bol):
   if bol==True:
@@ -51,27 +31,6 @@ def predict_death_event(feature1, feature2, feature3,feature4, feature5, feature
     y_pred = xgb_model_loaded.predict(df)[0]
     return y_pred
 
-# Function for updating metrics
-def update_metrics():
-    global test_data
-    test = test_data.sample(50)
-    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)
-
-
-@app.get("/metrics")
-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"
@@ -94,11 +53,5 @@ iface = gradio.Interface(fn = predict_death_event,
                          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)
+#iface.launch(debug=True)
+iface.launch(server_name = "0.0.0.0", server_port = 8001) # Ref. for parameters: https://www.gradio.app/docs/interface