Update app.py

app.py

@@ -107,13 +107,8 @@ def transcribe(microphone, file_upload, batch_size=15):
 
     return warn_output + text.strip(), system_info
 
-
-
-
-
 #              ------------summary section------------
 
-
 #          ------------for app integration later------------
 
 nlp = spacy.blank("nb")  # codename 'nb' = Norwegian Bokmål
@@ -145,9 +140,9 @@ def preprocess_text(text, file_upload):
 
 @spaces.GPU()
 def summarize_text(text, file_upload):
-#
-# ----add same if/elif logic as above here----
-#
+    #
+    # ----add same if/elif logic as above here----
+    #
     preprocessed_text = preprocess_text(text)
     inputs = summarization_model(preprocessed_text, max_length=1024, return_tensors="pt", truncation=True)
     inputs = inputs.to(device)
@@ -167,9 +162,9 @@ def build_similarity_matrix(sentences):
 # PageRank
 @spaces.GPU()
 def graph_based_summary(text, file_upload, num_paragraphs=3):
-#
-# ----add same if/elif logic as above here----
-#
+    #
+    # ----add same if/elif logic as above here----
+    #
     sentences = [sent.text for sent in doc.sents]
     if len(sentences) < num_paragraphs:
         return ' '.join(sentences)
@@ -211,7 +206,7 @@ def lex_rank_summary(text, file_upload, num_paragraphs=3, threshold=0.1):
 @spaces.GPU()
 def text_rank_summary(text, file_upload, num_paragraphs=3):
 
-    if (text is not None) and (file_upload is None):
+    if (text is not None) and (file_upload is not None):
         doc = nlp(text)
 
     elif (text is None) and (file_upload is not None):
@@ -235,9 +230,9 @@ def save_to_pdf(text, summary):
     pdf = FPDF()
     pdf.add_page()
     pdf.set_font("Arial", size=12)
-#
-# ----add same if/elif logic as above here----
-#
+    #
+    # ----add same if/elif logic as above here----
+    #
     if text:
         pdf.multi_cell(0, 10, "Text:\n" + text)
 
@@ -258,21 +253,20 @@ with iface:
     gr.Markdown(HEADER_INFO)
 
     with gr.Row():
-            gr.Markdown('''
-            ##### Here you will get transcription output
-            ##### ''')
-            microphone = gr.Audio(sources="microphone", type="filepath")
-            upload = gr.Audio(sources="upload", type="filepath")
-
+        gr.Markdown('''
+        ##### Here you will get transcription output
+        ##### ''')
+        microphone = gr.Audio(sources="microphone", type="filepath")
+        upload = gr.Audio(sources="upload", type="filepath")
 
-            transcribe_btn = gr.Button("Transcribe Interview")
-            text_output = gr.Textbox()
-            system_info = gr.Textbox(label="System Info")
+        transcribe_btn = gr.Button("Transcribe Interview")
+        text_output = gr.Textbox()
+        system_info = gr.Textbox(label="System Info")
 
-            transcribe_btn.click(transcribe_audio,
-                                [microphone, upload], [text_output]
-                                [system_info]
-                                )
+        transcribe_btn.click(transcribe_audio,
+                            [microphone, upload], [text_output]
+                            [system_info]
+                            )
 
     with gr.Tabs():
 
@@ -294,7 +288,6 @@ with iface:
             summarize_uploaded_button_graph.click(fn=graph_based_summary(file_upload), inputs=[text_input_graph], outputs=[summary_output_graph])
 
         with gr.TabItem("Summary | LexRank"):
-            with gr.Blocks():
             text_output = gr.Textbox(label="Transcription Output")
             text_input_lex = gr.Textbox(label="Input Text", placeholder="txt2summarize")
             summary_output_lex = gr.Textbox(label="LexRank | cosine similarity")
@@ -321,7 +314,6 @@ with iface:
             """)
             gr.Markdown("""
             *Bjørn*: **sammendrag basert på i de setningene som ligner mest på hverandre fra teksten**
-
             """)
 
             summarize_transcribed_button_text_rank = gr.Button("Summary of Transcribed Text, Click Here")
@@ -339,4 +331,4 @@ with iface:
 
             pdf_text_only.click(fn=lambda text: save_to_pdf(text, ""), inputs=[transcribed_text], outputs=[pdf_output])
             pdf_summary_only.click(fn=lambda summary: save_to_pdf("", summary), inputs=[summary_output_graph, summary_output_lex, summary_output_text_rank], outputs=[pdf_output])  # Includes all summary outputs
-            pdf_both.click(fn=lambda text, summary: save_to_pdf(text, summary), inputs=[transcribed_text, summary_output_graph], outputs=[pdf_output])
+            pdf_both.click(fn=lambda text, summary: save_to_pdf(text, summary), inputs=[transcribed_text, summary_output_graph], outputs=[pdf_output])