Update app.py

app.py

@@ -24,8 +24,8 @@ def proc_submission(
     summary_type: str,
     num_beams,
     token_batch_length,
-    length_penalty,
-    repetition_penalty,
+    #length_penalty,
+    #repetition_penalty,
     #no_repeat_ngram_size: int = 3,
     max_input_length: int = 768,
 ):
@@ -48,7 +48,7 @@ def proc_submission(
 
     settings = {
         "length_penalty": float(length_penalty),
-        "repetition_penalty": float(repetition_penalty),
+        "repetition_penalty": 3.5,#float(repetition_penalty),
         "no_repeat_ngram_size": 3,
         "encoder_no_repeat_ngram_size": 4,
         "num_beams": int(num_beams),
@@ -74,10 +74,10 @@ def proc_submission(
 
     _summaries = summarize_via_tokenbatches(
         tr_in,
-        model_led_det if (model_type == "LED" & summary_type == "detailed") else model_det,
-        tokenizer_led_det if (model_type == "LED" & summary_type == "detailed") else tokenizer_det,
-        model_led_tldr if (model_type == "LED" & summary_type == "tldr") else model_tldr,
-        tokenizer_led_tldr if (model_type == "LED" & summary_type == "tldr") else tokenizer_tldr,
+        model_led_det if (model_type == "LED" and summary_type == "Detailed") else model_det,
+        tokenizer_led_det if (model_type == "LED" and summary_type == "Detailed") else tokenizer_det,
+        model_led_tldr if (model_type == "LED" and summary_type == "TLDR") else model_tldr,
+        tokenizer_led_tldr if (model_type == "LED" and summary_type == "TLDR") else tokenizer_tldr,
         batch_length=token_batch_length,
         **settings,
     )
@@ -176,7 +176,7 @@ if __name__ == "__main__":
                     choices=["LongT5", "LED"], label="Model Architecture", value="LongT5"
                 )
                 num_beams = gr.Radio(
-                    choices=[2, 3, 4],
+                    choices=[2, 3, 4, 5, 6],
                     label="Beam Search: # of Beams",
                     value=2,
                 )
@@ -197,13 +197,13 @@ if __name__ == "__main__":
                     value=512,
                 )
 
-            with gr.Row():
-                repetition_penalty = gr.inputs.Slider(
-                    minimum=1.0,
-                    maximum=5.0,
-                    label="repetition penalty",
-                    default=3.5,
-                    step=0.1,
+            #with gr.Row():
+                #repetition_penalty = gr.inputs.Slider(
+                    #minimum=1.0,
+                    #maximum=5.0,
+                    #label="repetition penalty",
+                    #default=3.5,
+                    #step=0.1,
                 )
                 #no_repeat_ngram_size = gr.Radio(
                     #choices=[2, 3, 4],
@@ -285,7 +285,6 @@ if __name__ == "__main__":
                 num_beams,
                 token_batch_length,
                 length_penalty,
-                repetition_penalty,
             ],
             outputs=[output_text, summary_text, compression_rate],
         )