Update test.py

test.py

@@ -1,63 +1,63 @@
-import torch
-from torch.nn.functional import softmax
-from load_model import load_model  # Import the load_model function
-import numpy as np
-from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
-import streamlit as st
-
-@st.cache_resource
-def get_model_and_tokenizer(model_name):
-    return load_model(model_name)
-
-# Initialize default model (could be anything, or even load dynamically)
-default_model_name = "cahya/bert-base-indonesian-522M"
-tokenizer, model = load_model(default_model_name)
-
-# Prediction function
-def predict_hoax(title, content):
-    if tokenizer is None or model is None:
-        raise ValueError("Model and tokenizer must be loaded before prediction.")
-    
-    print(f"Using model: {model}")
-    print(f"Using tokenizer: {tokenizer}")
-
-    text = f"{title} [SEP] {content}"
-    inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=256)
-    with torch.no_grad():
-        outputs = model(**inputs)
-    probs = softmax(outputs.logits, dim=1)
-    pred = torch.argmax(probs, dim=1).item()
-    label = 'HOAX' if pred == 1 else 'NON-HOAX'
-    return label
-
-# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-# model.to(device)
-
-# LIME prediction function
-def predict_proba_for_lime(texts):
-    results = []
-    for text in texts:
-        inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=256)
-        with torch.no_grad():
-            outputs = model(**inputs)
-        probs = softmax(outputs.logits, dim=1).detach().cpu().numpy()
-        results.append(probs[0])
-    return np.array(results)
-
-def evaluate_model_performance(df, tokenizer, model):
-    true_labels = []
-    pred_labels = []
-
-    for index, row in df.iterrows():
-        true_label = row['Label']  # Menggunakan 'Title' sebagai label sebenarnya karena tidak ada 'Final_Result'
-        pred_label = predict_hoax(row['Title'], row['Content'])
-        
-        true_labels.append(1 if true_label == 'HOAX' else 0)
-        pred_labels.append(1 if pred_label == 'HOAX' else 0)
-
-    accuracy = accuracy_score(true_labels, pred_labels)
-    precision = precision_score(true_labels, pred_labels, average='binary')
-    recall = recall_score(true_labels, pred_labels, average='binary')
-    f1 = f1_score(true_labels, pred_labels, average='binary')
-
+import torch
+from torch.nn.functional import softmax
+from load_model import load_model  # Import the load_model function
+import numpy as np
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
+import streamlit as st
+
+@st.cache_resource
+def get_model_and_tokenizer(model_name):
+    return load_model(model_name)
+
+# Initialize default model (could be anything, or even load dynamically)
+default_model_name = "cahya/bert-base-indonesian-522M"
+tokenizer, model = load_model(default_model_name)
+
+# Prediction function
+def predict_hoax(title, content):
+    if tokenizer is None or model is None:
+        raise ValueError("Model and tokenizer must be loaded before prediction.")
+    
+    print(f"Using model: {model}")
+    print(f"Using tokenizer: {tokenizer}")
+
+    text = f"{title} [SEP] {content}"
+    inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=256)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    probs = softmax(outputs.logits, dim=1)
+    pred = torch.argmax(probs, dim=1).item()
+    label = 'HOAX' if pred == 1 else 'NON-HOAX'
+    return label
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# LIME prediction function
+def predict_proba_for_lime(texts):
+    results = []
+    for text in texts:
+        inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=256).to(device)
+        with torch.no_grad():
+            outputs = model(**inputs)
+        probs = softmax(outputs.logits, dim=1).detach().cpu().numpy()
+        results.append(probs[0])
+    return np.array(results)
+
+def evaluate_model_performance(df, tokenizer, model):
+    true_labels = []
+    pred_labels = []
+
+    for index, row in df.iterrows():
+        true_label = row['Label']  # Menggunakan 'Title' sebagai label sebenarnya karena tidak ada 'Final_Result'
+        pred_label = predict_hoax(row['Title'], row['Content'])
+        
+        true_labels.append(1 if true_label == 'HOAX' else 0)
+        pred_labels.append(1 if pred_label == 'HOAX' else 0)
+
+    accuracy = accuracy_score(true_labels, pred_labels)
+    precision = precision_score(true_labels, pred_labels, average='binary')
+    recall = recall_score(true_labels, pred_labels, average='binary')
+    f1 = f1_score(true_labels, pred_labels, average='binary')
+
     return accuracy, precision, recall, f1