setting unsafe true^Cor spinner

app.py

@@ -289,10 +289,8 @@ def load_model():
     # Load the model
     multiModel = MultimodalModel(bert_model_name, num_labels)
 
-    # Load the model weights directly from Hugging Face Spaces
+    # Load the model weights and tokenizer directly from Hugging Face Spaces
     multiModel.load_state_dict(torch.hub.load_state_dict_from_url(model_weights_path, map_location=device), strict=False)
-
-   # multiModel.load_state_dict(torch.load(file_path + "/MultiModal_model_state_dict.pth",map_location=device),strict=False)
     tokenizer = AutoTokenizer.from_pretrained("netgvarun2005/MultiModalBertHubertTokenizer") 
 
     # GenAI
@@ -421,7 +419,7 @@ def process_file(ser_model,tokenizer,gpt_model,gpt_tokenizer):
 
             try:
                 audio_array, sr = librosa.load(preprocessWavFile(temp_filename), sr=None)
-                with st.spinner(st.markdown("<p style='font-size: 16px; font-weight: bold;'>Generating transcriptions in the side pane! Please wait...</p>", unsafe_allow_html=False)):
+                with st.spinner(st.markdown("<p style='font-size: 16px; font-weight: bold;'>Generating transcriptions in the side pane! Please wait...</p>", unsafe_allow_html=True)):
                     transcription = speechtoText(temp_filename)
                     emo = predict(audio_array,ser_model,2,tokenizer,transcription)
                     # Display the transcription in a textbox
@@ -436,7 +434,7 @@ def process_file(ser_model,tokenizer,gpt_model,gpt_tokenizer):
             # Store the value of emo in the session state
             st.session_state.emo = emo
             if st.button(button_label):
-                with st.spinner(st.markdown("<p style='font-size: 16px; font-weight: bold;'>Generating tips (it may take upto 3-4 mins depending upon network speed). Please wait...</p>", unsafe_allow_html=False)):
+                with st.spinner(st.markdown("<p style='font-size: 16px; font-weight: bold;'>Generating tips (it may take upto 3-4 mins depending upon network speed). Please wait...</p>", unsafe_allow_html=True)):
                     # Retrieve prompt from the emotion
                     emo = st.session_state.emo
                     # Call the function for GENAI