Update main.py

main.py

@@ -11,14 +11,14 @@ from sklearn.feature_extraction.text import TfidfVectorizer
 def load_models():
     st.session_state.loaded = True
 
-    with open('models/tfidf_vectorizer_svm_model_2_classes_gpt_chatgpt_detection_tfidf_bg_0.886_F1_score.pkl', 'rb') as f:
-        st.session_state.tfidf_vectorizer_disinformation = pickle.load(f)
+    # with open('models/tfidf_vectorizer_svm_model_2_classes_gpt_chatgpt_detection_tfidf_bg_0.886_F1_score.pkl', 'rb') as f:
+    #     st.session_state.tfidf_vectorizer_disinformation = 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_2_classes_gpt_chatgpt_detection_tfidf_bg_0.886_F1_score.pkl', 'rb') as f:
-        st.session_state.gpt_detector = pickle.load(f)
+    # with open('models/svm_model_2_classes_gpt_chatgpt_detection_tfidf_bg_0.886_F1_score.pkl', 'rb') as f:
+    #     st.session_state.gpt_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)
@@ -55,12 +55,14 @@ if all([
     # st.session_state.gpt_detector_result = ''
     # st.session_state.gpt_detector_probability = [1, 0]
 
+    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.bert_disinfo_result = [{'label': '', 'score': 1}]
         
-    st.session_state.bert_gpt_result = [{'label': '', 'score': 1}]
+
 
 content = load_content()
 if 'loaded' not in st.session_state:
@@ -101,6 +103,8 @@ if st.session_state.agree:
             # st.session_state.gpt_detector_result = st.session_state.gpt_detector.predict(user_tfidf_disinformation)[0]
             # st.session_state.gpt_detector_probability = st.session_state.gpt_detector.predict_proba(user_tfidf_disinformation)[0]
 
+            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]
@@ -108,7 +112,7 @@ if st.session_state.agree:
 
             st.session_state.bert_disinfo_result = st.session_state.bert_disinfo(user_input)
 
-            st.session_state.bert_gpt_result = st.session_state.bert_gpt(user_input)
+            
     
         # if st.session_state.gpt_detector_result == 1:
         #     st.warning(content['gpt_getect_yes'][st.session_state.lang] +
@@ -119,6 +123,15 @@ if st.session_state.agree:
         #                str(round(st.session_state.gpt_detector_probability[0] * 100, 2)) +
         #                content['gpt_no_proba'][st.session_state.lang], icon="✅")
 
+        if st.session_state.bert_gpt_result[0]['label'] == 'LABEL_1':
+            st.warning(content['bert_gpt_1'][st.session_state.lang] +
+                       str(round(st.session_state.bert_gpt_result[0]['score'] * 100, 2)) +
+                       content['bert_gpt_2'][st.session_state.lang], icon = "⚠️")
+        else:
+            st.success(content['bert_human_1'][st.session_state.lang] +
+                       str(round(st.session_state.bert_gpt_result[0]['score'] * 100, 2)) +
+                       content['bert_human_2'][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)) +
@@ -137,14 +150,6 @@ if st.session_state.agree:
                        str(round(st.session_state.bert_disinfo_result[0]['score'] * 100, 2)) +
                        content['bert_no_2'][st.session_state.lang], icon="✅")
 
-        if st.session_state.bert_gpt_result[0]['label'] == 'LABEL_1':
-            st.warning(content['bert_gpt_1'][st.session_state.lang] +
-                       str(round(st.session_state.bert_gpt_result[0]['score'] * 100, 2)) +
-                       content['bert_gpt_2'][st.session_state.lang], icon = "⚠️")
-        else:
-            st.success(content['bert_human_1'][st.session_state.lang] +
-                       str(round(st.session_state.bert_gpt_result[0]['score'] * 100, 2)) +
-                       content['bert_human_2'][st.session_state.lang], icon="✅")
     
         st.info(content['disinformation_definition'][st.session_state.lang], icon="ℹ️")