Update app.py

app.py

@@ -203,94 +203,18 @@ def text_rank_summary(text, num_paragraphs=3):
     ranked_sentences = sorted(((scores[i], s) for i, s in enumerate(sentences)), reverse=True)
     return ' '.join([ranked_sentences[i][1] for i in range(num_paragraphs)])
 
-
-# Save text+summary/PDF
-def save_to_pdf(text, summary):
-    pdf = FPDF()
-    pdf.add_page()
-    pdf.set_font("Arial", size=12)
-
-    if text:
-        pdf.multi_cell(0, 10, "Text:\n" + text)
-
-    pdf.ln(10)  # Paragraph space
-
-    if summary:
-        pdf.multi_cell(0, 10, "Summary:\n" + summary)
-
-    pdf_output_path = "transcription.pdf"
-    pdf.output(pdf_output_path)
-    return pdf_output_path
-
 iface = gr.Blocks()
 
 with iface:
-    
     gr.HTML(SIDEBAR_INFO)
     gr.Markdown(HEADER_INFO)
-
-    with gr.Tabs():
-
-        with gr.TabItem("Summary | PageRank"):
-            text_input_graph = gr.Textbox(label="Input Text", placeholder="txt2summarize")
-            summary_output_graph = gr.Textbox(label="PageRank | token-based similarity")
-        
-            gr.Markdown("""
-            **token-based**: similarity matrix edge weights representing token overlap/
-            ranked by their centrality in the graph (good with dense inter-sentence relationships)
-            """)
-            gr.Markdown("""
-            *Bjørn*: **gir sammendrag som fanger opp de mest relevante setninger i teksten**
-            """)
-        
-            summarize_transcribed_button_graph = gr.Button("Summary of Transcribed Text, Click Here")
-            summarize_transcribed_button_graph.click(fn=lambda text: graph_based_summary(text), inputs=[text_output], outputs=[summary_output_graph])
-            summarize_uploaded_button_graph = gr.Button("Upload Text to Summarize, Click Here")
-            summarize_uploaded_button_graph.click(fn=graph_based_summary, inputs=[text_input_graph], outputs=[summary_output_graph])
-
-        with gr.TabItem("Summary | LexRank"):
-            text_input_lex = gr.Textbox(label="Input Text", placeholder="txt2summarize")
-            summary_output_lex = gr.Textbox(label="LexRank | cosine similarity")
-        
-            gr.Markdown("""
-            **semantic**: TF-IDF vectorization@cosine similarity matrix, ranked by eigenvector centrality.
-            (good for sparse graph structures with thresholding)
-            """)
-            gr.Markdown("""
-            *Bjørn*: **gir sammendrag som best fanger opp betydningen av hele teksten**
-            """)
-        
-            summarize_transcribed_button_lex = gr.Button("Summary of Transcribed Text, Click Here")
-            summarize_transcribed_button_lex.click(fn=lambda text: lex_rank_summary(text), inputs=[text_output], outputs=[summary_output_lex])
-            summarize_uploaded_button_lex = gr.Button("Upload Text to Summarize, Click Here")
-            summarize_uploaded_button_lex.click(fn=lex_rank_summary, inputs=[text_input_lex], outputs=[summary_output_lex])
-
-        with gr.TabItem("Summary | TextRank"):
-            text_input_text_rank = gr.Textbox(label="Input Text", placeholder="txt2summarize")
-            summary_output_text_rank = gr.Textbox(label="TextRank | lexical similarity")
-        
-            gr.Markdown("""
-            **sentence**: graph with weighted edges based on lexical similarity. (i.e" "sentence similarity"word overlap)/sentence similarity
-            """)
-            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")
-            summarize_transcribed_button_text_rank.click(fn=lambda text: text_rank_summary(text), inputs=[text_output], outputs=[summary_output_text_rank])
-            summarize_uploaded_button_text_rank = gr.Button("Upload Text to Summarize, Click Here")
-            summarize_uploaded_button_text_rank.click(fn=text_rank_summary, inputs=[text_input_text_rank], outputs=[summary_output_text_rank])
-
-        with gr.TabItem("Download PDF"):
-            pdf_text_only = gr.Button("Download PDF with Text Only")
-            pdf_summary_only = gr.Button("Download PDF with Summary Only")
-            pdf_both = gr.Button("Download PDF with Both")
-
-            pdf_output = gr.File(label="Download PDF")
-
-            pdf_text_only.click(fn=lambda text: save_to_pdf(text, ""), inputs=[text_output], 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=[text_output, summary_output_graph], outputs=[pdf_output])  # Defaulting to Graph-based summary
+    
+    audio_input = gr.Audio(label="Upload Audio File")
+    transcribed_text = gr.Textbox(label="Transcribed Text")
+    system_info = gr.Textbox(label="System Info")
+    
+    transcribe_button = gr.Button("Transcribe")
+    transcribe_button.click(fn=transcribe_audio, inputs=audio_input, outputs=[transcribed_text, system_info])
 
 iface.launch(share=True, debug=True)
+