Update app.py

app.py

@@ -71,12 +71,12 @@ if __name__ == "__main__":
     st.sidebar.image(img_short)
     st.image(img_full)
     st.title("VLT5 - Reason for Contact generator")
-    st.markdown("### RfC Generation model.")
-    st.markdown("-- *Input*: Whole conversation. Should specify roles (e.g. **AGENT: Hello, how can I help you? CLIENT: Hi, I would like to report a stolen card.**")
-    st.markdown("-- *Output*: Reason for calling for the whole conversation.")
-    st.markdown("### RfC Detection model.")
-    st.markdown("-- *Input*: A single turn from the conversation e.g. **'Hello, how can I help you?'** or **'Hi, I would like to report a stolen card.'**")
-    st.markdown("-- *Output*: Model will return an empty string if a turn possibly does not includes Reason for Calling, or a sentence if the RfC is detected.")
+    st.markdown("#### RfC Generation model.")
+    st.markdown("**Input***: Whole conversation. Should specify roles (e.g. *AGENT: Hello, how can I help you? CLIENT: Hi, I would like to report a stolen card.* Put a whole conversation or full e-mail here.")
+    st.markdown("**Output**: Reason for calling for the whole conversation.")
+    st.markdown("#### RfC Detection model.")
+    st.markdown("**Input**: A single turn from the conversation e.g. *'Hello, how can I help you?'* or *'Hi, I would like to report a stolen card.'. Put a single turn or a few sentences here.*")
+    st.markdown("**Output**: Model will return an empty string if a turn possibly does not includes Reason for Calling, or a sentence if the RfC is detected.")
     
     generated_rfc = ""
     user_input = st.text_area(
@@ -99,10 +99,6 @@ if __name__ == "__main__":
     result = st.button("Find reason for contact")
 
     if result:
-        if mode == "Polish - RfC Generation (accepts whole conversation)":
-            text_area = "Put a whole conversation or full e-mail here."
-        elif mode == "Polish - RfC Detection (accepts one turn)":
-            text_area = "Put a single turn or a few sentences here."
         generated_rfc = get_predictions(text=user_input, mode=mode)
-        st.text_area(text_area, generated_rfc)
+        st.text_area("Find reason for contact", generated_rfc)
         print(f"Input: {user_input} ---> Reason for contact: {generated_rfc}")