Update main.py

main.py

@@ -11,11 +11,11 @@ from sklearn.feature_extraction.text import TfidfVectorizer
 def load_models():
     st.session_state.loaded = True
 
-    with open('models/tfidf_vectorizer_untrue_inform_detection_tfidf_bg_0.96_F1_score_3Y_N_Q1_082023.pkl', 'rb') as f:
-        st.session_state.tfidf_vectorizer_untrue_inf = pickle.load(f)
+    # with open('models/tfidf_vectorizer_untrue_inform_detection_tfidf_bg_0.96_F1_score_3Y_N_Q1_082023.pkl', 'rb') as f:
+    #     st.session_state.tfidf_vectorizer_untrue_inf = pickle.load(f)
 
-    with open('models/SVM_model_untrue_inform_detection_tfidf_bg_0.96_F1_score_3Y_N_Q1_082023.pkl', 'rb') as f:
-        st.session_state.untrue_detector = pickle.load(f)
+    # with open('models/SVM_model_untrue_inform_detection_tfidf_bg_0.96_F1_score_3Y_N_Q1_082023.pkl', 'rb') as f:
+    #     st.session_state.untrue_detector = pickle.load(f)
 
     st.session_state.bert_disinfo = pipeline(task="text-classification",
                                      model=BertForSequenceClassification.from_pretrained("usmiva/bert-desinform-bg", num_labels=2),
@@ -48,14 +48,14 @@ if 'lang' not in st.session_state:
 
 if all([
     'bert_gpt_result' not in st.session_state,
-    'untrue_detector_result' not in st.session_state,
+    # 'untrue_detector_result' not in st.session_state,
     'bert_disinfo_result' not in st.session_state,
     'emotions_result' not in st.session_state
     ]):
     st.session_state.bert_gpt_result = [{'label': '', 'score': 1}]
 
-    st.session_state.untrue_detector_result = ''
-    st.session_state.untrue_detector_probability = 1
+    # st.session_state.untrue_detector_result = ''
+    # st.session_state.untrue_detector_probability = 1
     
     st.session_state.bert_disinfo_result = [{'label': '', 'score': 1}]
         
@@ -98,10 +98,10 @@ if st.session_state.agree:
         if st.button(content['analyze_button'][st.session_state.lang]):
             st.session_state.bert_gpt_result = st.session_state.bert_gpt(user_input)
             
-            user_tfidf_untrue_inf = st.session_state.tfidf_vectorizer_untrue_inf.transform([user_input])
-            st.session_state.untrue_detector_result = st.session_state.untrue_detector.predict(user_tfidf_untrue_inf)[0]
-            st.session_state.untrue_detector_probability = st.session_state.untrue_detector.predict_proba(user_tfidf_untrue_inf)[0]
-            st.session_state.untrue_detector_probability = max(st.session_state.untrue_detector_probability[0], st.session_state.untrue_detector_probability[1]) 
+            # user_tfidf_untrue_inf = st.session_state.tfidf_vectorizer_untrue_inf.transform([user_input])
+            # st.session_state.untrue_detector_result = st.session_state.untrue_detector.predict(user_tfidf_untrue_inf)[0]
+            # st.session_state.untrue_detector_probability = st.session_state.untrue_detector.predict_proba(user_tfidf_untrue_inf)[0]
+            # st.session_state.untrue_detector_probability = max(st.session_state.untrue_detector_probability[0], st.session_state.untrue_detector_probability[1]) 
 
             st.session_state.bert_disinfo_result = st.session_state.bert_disinfo(user_input)
             
@@ -118,14 +118,14 @@ if st.session_state.agree:
                        str(round(st.session_state.bert_gpt_result[0]['score'] * 100, 2)) +
                        content['bert_human_prob'][st.session_state.lang], icon="✅")
         
-        if st.session_state.untrue_detector_result == 0:
-            st.warning(content['untrue_getect_yes'][st.session_state.lang] +
-                       str(round(st.session_state.untrue_detector_probability * 100, 2)) +
-                       content['untrue_yes_proba'][st.session_state.lang], icon="⚠️")
-        else:
-            st.success(content['untrue_getect_no'][st.session_state.lang] +
-                       str(round(st.session_state.untrue_detector_probability * 100, 2)) +
-                       content['untrue_no_proba'][st.session_state.lang], icon="✅")
+        # if st.session_state.untrue_detector_result == 0:
+        #     st.warning(content['untrue_getect_yes'][st.session_state.lang] +
+        #                str(round(st.session_state.untrue_detector_probability * 100, 2)) +
+        #                content['untrue_yes_proba'][st.session_state.lang], icon="⚠️")
+        # else:
+        #     st.success(content['untrue_getect_no'][st.session_state.lang] +
+        #                str(round(st.session_state.untrue_detector_probability * 100, 2)) +
+        #                content['untrue_no_proba'][st.session_state.lang], icon="✅")
     
         if st.session_state.bert_disinfo_result[0]['label'] == 'LABEL_1':
             st.warning(content['bert_yes_1'][st.session_state.lang] +