Update app.py

app.py

@@ -6,7 +6,7 @@ import json
 import numpy as np
 # from fastapi import FastAPI,Response
 # from sklearn.metrics import accuracy_score, f1_score
-# import prometheus_client as prom
+import prometheus_client as prom
 import pandas as pd
 # import uvicorn
 import os
@@ -21,26 +21,58 @@ import torch
 # app=FastAPI()
 
 
-# test_data=pd.read_csv("test.csv")
+test_data=pd.read_csv("test.csv")
 
 
-# f1_metric = prom.Gauge('death_f1_score', 'F1 score for test samples')
+f1_metric = prom.Gauge('bertscore_f1_score', 'F1 score for captions')
 
 # Function for updating metrics
-# def update_metrics():
-#     test = test_data.sample(20)
-#     X = test.iloc[:, :-1].values
-#     y = test['DEATH_EVENT'].values
+
+
+def update_metrics():
+    # test = test_data.sample(20)
+    # X = test.iloc[:, :-1].values
+    # y = test['DEATH_EVENT'].values
     
-#     # test_text = test['Text'].values
-#     test_pred = loaded_model.predict(X)
-#     #pred_labels = [int(pred['label'].split("_")[1]) for pred in test_pred]
+    # test_text = test['Text'].values
+    # test_pred = loaded_model.predict(X)
+    #pred_labels = [int(pred['label'].split("_")[1]) for pred in test_pred]
+
+    # f1 = f1_score( y , test_pred).round(3)
+
+    #f1 = f1_score(test['labels'], pred_labels).round(3)
+
+    # f1_metric.set(f1)
+
+
+
+    # dict_metric_scores = {}
+
+    labels_ids = eval_pred.label_ids
+    pred_ids = eval_pred.predictions
 
-#     f1 = f1_score( y , test_pred).round(3)
+    # all unnecessary tokens are removed
+    pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
+    labels_ids[labels_ids == -100] = tokenizer.pad_token_id
+    label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)
 
-#     #f1 = f1_score(test['labels'], pred_labels).round(3)
+    # calculating various metrics
+    rouge_output = dict_metrics["rouge2"].compute(predictions=pred_str, references=label_str, rouge_types=["rouge2"])
+    dict_metric_scores["rouge2_score"] = rouge_output['rouge2']
 
-#     f1_metric.set(f1)
+
+
+    bertscore_output = dict_metrics["bertscore"].compute(predictions=pred_str, references=label_str, lang="en")
+
+    bert_f1_metric = bertscore_output['f1']
+    f1_metric.set(bert_f1_metric)
+
+
+
+    # return dict_metric_scores
+
+#bertscore or rougue
+    
 
 
 
@@ -101,10 +133,10 @@ def predict_event(image):
 
 
 
-# @app.get("/metrics")
-# async def get_metrics():
-#     update_metrics()
-#     return Response(media_type="text/plain", content= prom.generate_latest())
+@app.get("/metrics")
+async def get_metrics():
+    update_metrics()
+    return Response(media_type="text/plain", content= prom.generate_latest())