Update tasks/text.py

tasks/text.py

@@ -51,11 +51,11 @@ async def evaluate_text(request: TextEvaluationRequest):
     #train_test = dataset.train_test_split(test_size=.33, seed=42)
     train_test = dataset["train"].train_test_split(test_size=request.test_size, seed=request.test_seed)
     test_dataset = train_test["test"]
-
-    tfidf_vect = TfidfVectorizer(stop_words = 'english')
+ 
+    #tfidf_vect = TfidfVectorizer(stop_words = 'english')
     
-    tfidf_train = tfidf_vect.fit_transform(train_dataset['quote'])
-    tfidf_test = tfidf_vect.transform(test_dataset['quote'])
+    #tfidf_train = tfidf_vect.fit_transform(train_dataset['quote'])
+    #tfidf_test = tfidf_vect.transform(test_dataset['quote'])
 
 
     # Start tracking emissions
@@ -69,11 +69,13 @@ async def evaluate_text(request: TextEvaluationRequest):
     
     # Make random predictions (placeholder for actual model inference)
     true_labels = test_dataset["label"]
+    
+    predictions = [random.randint(0, 7) for _ in range(len(true_labels))]
 
-    LR = LogisticRegression(class_weight='balanced', max_iter=20, random_state=1234,
+    #LR = LogisticRegression(class_weight='balanced', max_iter=20, random_state=1234,
                    solver='liblinear')
-    LR.fit(pd.DataFrame.sparse.from_spmatrix(tfidf_train), pd.DataFrame(y_train_v))
-    predictions=LR.predict(pd.DataFrame.sparse.from_spmatrix(tfidf_test))
+    #LR.fit(pd.DataFrame.sparse.from_spmatrix(tfidf_train), pd.DataFrame(y_train_v))
+    #predictions=LR.predict(pd.DataFrame.sparse.from_spmatrix(tfidf_test))
     #--------------------------------------------------------------------------------------------
     # YOUR MODEL INFERENCE STOPS HERE
     #--------------------------------------------------------------------------------------------