app.py analyse function fix 4

src/app.py

@@ -42,17 +42,17 @@ st.set_page_config(
     layout="wide"
 )
 
-# Initialize session state
+# Initialize session state variables
 if 'model' not in st.session_state:
-    st.session_state.model = None
+    st.session_state['model'] = None
 if 'tokenizer' not in st.session_state:
-    st.session_state.tokenizer = None
+    st.session_state['tokenizer'] = None
 if 'preprocessor' not in st.session_state:
-    st.session_state.preprocessor = None
+    st.session_state['preprocessor'] = None
 
 def load_model():
     """Load the trained model and tokenizer."""
-    if st.session_state.model is None:
+    if st.session_state['model'] is None:
         # Initialize model
         model = HybridFakeNewsDetector(
             bert_model_name=BERT_MODEL_NAME,
@@ -71,24 +71,24 @@ def load_model():
         # Load the filtered state dict
         model.load_state_dict(filtered_state_dict, strict=False)
         model.eval()
-        st.session_state.model = model
+        st.session_state['model'] = model
         
         # Initialize tokenizer
-        st.session_state.tokenizer = BertTokenizer.from_pretrained(BERT_MODEL_NAME)
+        st.session_state['tokenizer'] = BertTokenizer.from_pretrained(BERT_MODEL_NAME)
         
         # Initialize preprocessor
-        st.session_state.preprocessor = TextPreprocessor()
+        st.session_state['preprocessor'] = TextPreprocessor()
 
 def predict_news(text):
     """Predict if the given news is fake or real."""
-    if st.session_state.model is None:
+    if st.session_state['model'] is None:
         load_model()
     
     # Preprocess text
-    processed_text = st.session_state.preprocessor.preprocess_text(text)
+    processed_text = st.session_state['preprocessor'].preprocess_text(text)
     
     # Tokenize
-    encoding = st.session_state.tokenizer.encode_plus(
+    encoding = st.session_state['tokenizer'].encode_plus(
         processed_text,
         add_special_tokens=True,
         max_length=MAX_SEQUENCE_LENGTH,
@@ -100,7 +100,7 @@ def predict_news(text):
     
     # Get prediction
     with torch.no_grad():
-        outputs = st.session_state.model(
+        outputs = st.session_state['model'](
             encoding['input_ids'],
             encoding['attention_mask']
         )