Update app.py

app.py

@@ -46,6 +46,7 @@ def get_predictions(text, mode):
             num_beam_groups=3,
             min_length=10,
             max_length=100,
+            diversity_penalty=1.0,
         )
     elif mode == "Polish - RfC Detection":
         output = model_det_pl.generate(
@@ -70,8 +71,14 @@ if __name__ == "__main__":
     st.sidebar.image(img_short)
     st.image(img_full)
     st.title("VLT5 - Reason for Contact generator")
-
-    generated_keywords = ""
+    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.")
+    
+    generated_rfc = ""
     user_input = st.text_area(
         label=f"Input text (max {max_length} characters)",
         value="",
@@ -90,19 +97,12 @@ if __name__ == "__main__":
     )
 
     result = st.button("Find reason for contact")
-    if mode == "Polish - RfC Generation (accepts whole conversation)":
-        st.markdown("### You selected 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.")
-        text_area = "Put a whole conversation or full e-mail here."
-        
-    elif mode == "Polish - RfC Detection (accepts one turn)":
-        st.markdown("### You selected 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.")
-        text_area = "Put a single turn or a few sentences here."
-        
+
     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)
         print(f"Input: {user_input} ---> Reason for contact: {generated_rfc}")