Update tasks/text.py

tasks/text.py

@@ -62,32 +62,80 @@ async def evaluate_text(request: TextEvaluationRequest):
 
     from transformers import AutoModelForSequenceClassification, AutoTokenizer
     import torch
+    from torch.utils.data import DataLoader
   
     # Load model and tokenizer from Hugging Face Hub
     MODEL_REPO = "ClimateDebunk/FineTunedDistilBert4SeqClass"
 
     tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased', do_lower_case=True)
+    MAX_LENGTH = 365
+
     model = AutoModelForSequenceClassification.from_pretrained(MODEL_REPO)
+    #model.eval()  # Set to evaluation mode
 
-    model.eval()  # Set to evaluation mode
+    class QuotesDataset(Dataset):
+        def __init__(self, encodings, labels):
+            self.encodings = encodings
+            self.labels = labels
+    
+        def __getitem__(self, idx):
+            item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
+            item['labels'] = torch.tensor(self.labels[idx], dtype=torch.long)
+            return item
     
-    def preprocess(texts):
-        """ Tokenize text inputs for DistilBERT """
-        return tokenizer(texts, padding='max_length', truncation=True, max_length=365, return_tensors="pt")
+        def __len__(self):
+            return len(self.labels)
+
+    def encode_data(tokenizer, texts, labels, max_length):
+        try:
+            if isinstance(texts, pd.Series):
+                texts = texts.tolist()
+            if isinstance(labels, pd.Series):
+                labels = labels.tolist()
+                
+            encodings = tokenizer(texts, truncation=True, padding='max_length', max_length=max_length, return_tensors='pt')
+            return QuotesDataset(encodings, labels)
+    
+        except Exception as e:
+            print(f"Error during tokenization: {e}")
+            return None
+            
+    val_dataset = encode_data(tokenizer, test_dataset['quote'], test_dataset['label'], MAX_LENGTH)        
+    val_loader = DataLoader(val_dataset, batch_size= batch_size, shuffle=False) 
+  
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Using device: {device}")
     
-    def predict(texts):
-        """ Run inference using the fine-tuned DistilBERT model """
-        inputs = preprocess(texts)
+    def validate_model(model, val_loader, device):
+        model.eval()
+        predictions = []
         with torch.no_grad():
-            outputs = model(**inputs)
-            predictions = torch.argmax(outputs.logits, dim=1).tolist()
+            for batch in val_loader:
+                batch = {k: v.to(device) for k, v in batch.items()}
+                outputs = model(**batch)
+                preds = torch.argmax(outputs.logits, dim=-1)
+                predictions.extend(preds.cpu().numpy())
         return predictions
+
     
-    # Run inference
-    texts = test_dataset["quote"]
-    predictions = predict(texts)
+    # tokenize texts
+    #test_encodings = tokenizer(test_dataset["quote"], padding='max_length', truncation=True, max_length=MAX_LENGTH, return_tensors="pt")
+    #test_labels = torch.tensor(test_dataset["label"])
+
+    #test_dataset = TensorDataset(test_encodings["input_ids"], test_encodings["attention_mask"], test_labels)
+    #test_loader = DataLoader(test_dataset, batch_size=16)
     
+    #predictions = []
+    #with torch.no_grad():
+    #for batch in test_loader:
+    #    input_ids, attention_mask, labels = [x.to(device) for x in batch]
+    #    outputs = model(input_ids, attention_mask=attention_mask)
+    #   predictions = torch.argmax(outputs.logits, dim=1)
+
+    predictions = validate_model(model, val_loader, device)
     true_labels = test_dataset["label"]
+
     #--------------------------------------------------------------------------------------------
     # YOUR MODEL INFERENCE STOPS HERE
     #--------------------------------------------------------------------------------------------