Update app.py

app.py

@@ -228,7 +228,7 @@ def proc_submission(
         "repetition_penalty": float(repetition_penalty),
         "no_repeat_ngram_size": int(no_repeat_ngram_size),
         "encoder_no_repeat_ngram_size": 4,
-        "num_beams": int(num_beams),
+        "num_beams": 3,
         "min_length": 4,
         "max_length": int(token_batch_length // 4),
         "early_stopping": True,
@@ -488,53 +488,59 @@ if __name__ == "__main__":
     logger.info("Starting app instance")
     with demo:
         with gr.Column():
-            gr.Markdown("# Document Summarization with Long-Document Transformers")
-            gr.Markdown(
-                """An example use case for fine-tuned long document transformers. Model(s) are trained on [book summaries](https://hf.co/datasets/kmfoda/booksum). Architectures [in this demo](https://hf.co/spaces/pszemraj/document-summarization) are [LongT5-base](https://hf.co/pszemraj/long-t5-tglobal-base-16384-book-summary) and [Pegasus-X-Large](https://hf.co/pszemraj/pegasus-x-large-book-summary).
+            gr.Markdown("# Anushandhan Mitra")
+            # gr.Markdown(
+            #     """An example use case for fine-tuned long document transformers. Model(s) are trained on [book summaries](https://hf.co/datasets/kmfoda/booksum). Architectures [in this demo](https://hf.co/spaces/pszemraj/document-summarization) are [LongT5-base](https://hf.co/pszemraj/long-t5-tglobal-base-16384-book-summary) and [Pegasus-X-Large](https://hf.co/pszemraj/pegasus-x-large-book-summary).
     
-                **Want more performance? Run this demo from a free Google Colab GPU:**.
-                <br>
-                <a href="https://colab.research.google.com/gist/pszemraj/52f67cf7326e780155812a6a1f9bb724/document-summarization-on-gpu.ipynb">
-                <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
-                </a>
-                <br>
-                """
-            )
+            #     **Want more performance? Run this demo from a free Google Colab GPU:**.
+            #     <br>
+            #     <a href="https://colab.research.google.com/gist/pszemraj/52f67cf7326e780155812a6a1f9bb724/document-summarization-on-gpu.ipynb">
+            #     <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+            #     </a>
+            #     <br>
+            #     """
+            # )
             gr.Markdown("## Load Inputs & Select Parameters")
             gr.Markdown(
                 """Enter/paste text below, or upload a file. Pick a model & adjust params (_optional_), and press **Summarize!**
 
-                See [the guide doc](https://gist.github.com/pszemraj/722a7ba443aa3a671b02d87038375519) for details.
+                # See [the guide doc](https://gist.github.com/pszemraj/722a7ba443aa3a671b02d87038375519) for details.
                 """
             )
             with gr.Row(variant="compact"):
-                with gr.Column(scale=0.5, variant="compact"):
+                # with gr.Column(scale=0.5, variant="compact"):
+                #     model_name = gr.Dropdown(
+                #         choices=MODEL_OPTIONS,
+                #         value=MODEL_OPTIONS[0],
+                #         label="Model Name",
+                #     )
+                #     num_beams = gr.Radio(
+                #         choices=BEAM_OPTIONS,
+                #         value=BEAM_OPTIONS[len(BEAM_OPTIONS) // 2],
+                #         label="Beam Search: # of Beams",
+                #     )
+                #     load_examples_button = gr.Button(
+                #         "Load Example in Dropdown",
+                #     )
+                #     load_file_button = gr.Button("Upload & Process File")
+                with gr.Column(variant="compact"):
                     model_name = gr.Dropdown(
                         choices=MODEL_OPTIONS,
                         value=MODEL_OPTIONS[0],
                         label="Model Name",
                     )
-                    num_beams = gr.Radio(
-                        choices=BEAM_OPTIONS,
-                        value=BEAM_OPTIONS[len(BEAM_OPTIONS) // 2],
-                        label="Beam Search: # of Beams",
-                    )
-                    load_examples_button = gr.Button(
-                        "Load Example in Dropdown",
-                    )
-                    load_file_button = gr.Button("Upload & Process File")
-                with gr.Column(variant="compact"):
-                    example_name = gr.Dropdown(
-                        _examples,
-                        label="Examples",
-                        value=random.choice(_examples),
-                    )
+                    # example_name = gr.Dropdown(
+                    #     _examples,
+                    #     label="Examples",
+                    #     value=random.choice(_examples),
+                    # )
                     uploaded_file = gr.File(
                         label="File Upload",
                         file_count="single",
                         file_types=[".txt", ".md", ".pdf"],
                         type="file",
                     )
+                    load_file_button = gr.Button("Upload & Process File")
             with gr.Row():
                 input_text = gr.Textbox(
                     lines=4,
@@ -544,16 +550,16 @@ if __name__ == "__main__":
                 )
         gr.Markdown("---")
         with gr.Column():
-            gr.Markdown("## Generate Summary")
+            # gr.Markdown("## Generate Summary")
             with gr.Row():
                 summarize_button = gr.Button(
-                    "Summarize!",
+                    "Generate Summary!",
                     variant="primary",
                 )
-                gr.Markdown(
-                    "_Summarization should take ~1-2 minutes for most settings, but may extend up to 5-10 minutes in some scenarios._"
-                )
-            output_text = gr.HTML("<p><em>Output will appear below:</em></p>")
+                # gr.Markdown(
+                #     "_Summarization should take ~1-2 minutes for most settings, but may extend up to 5-10 minutes in some scenarios._"
+                # )
+            output_text = gr.HTML("<p><em>_Summarization should take ~1-2 minutes for most settings, but may extend up to 5-10 minutes in some scenarios._</em></p>")
             with gr.Column():
                 gr.Markdown("### Results & Scores")
                 with gr.Row():