Update app.py

app.py

@@ -3,6 +3,7 @@ import gradio as gr
 from transformers import pipeline
 import spacy
 import lib.read_pdf
+
 # Initialize spaCy model
 nlp = spacy.load('en_core_web_sm')
 nlp.add_pipe('sentencizer')
@@ -85,62 +86,61 @@ with gr.Blocks() as demo:
                 stored_paragraphs_1, stored_paragraphs_2 = extract_and_summarize(pdf1, pdf2)
                 updated_dropdown_1 = [f"Paragraph {i+1}: {p[:100]}..." for i, p in enumerate(stored_paragraphs_1)]
                 updated_dropdown_2 = [f"Paragraph {i+1}: {p[:100]}..." for i, p in enumerate(stored_paragraphs_2)]
-                return gr.update(choices=updated_dropdown_1),gr.update(choices=updated_dropdown_2)
+                return gr.update(choices=updated_dropdown_1), gr.update(choices=updated_dropdown_2)
 
             b1.click(fn=update_paragraphs, inputs=[pdf1, pdf2], outputs=[paragraph_1_dropdown, paragraph_2_dropdown])
 
-with gr.Row():
-    # Process the selected paragraph from PDF 1
-    with gr.Column():
-        gr.Markdown("### PDF 1 Analysis")
-        selected_paragraph_1 = gr.Textbox(label="Selected Paragraph 1 Content", lines=4)
-        summarize_btn1 = gr.Button("Summarize Text from PDF 1")
-        sentiment_btn1 = gr.Button("Classify Financial Tone from PDF 1")
-        summary_textbox_1 = gr.Textbox(label="Summary for PDF 1", lines=4)
-        sentiment_textbox_1 = gr.Textbox(label="Classification for PDF 1", lines=4)
-        fin_spans_1 = gr.HighlightedText(label="Financial Tone Analysis for PDF 1")
-
-        def process_paragraph_1(paragraph):
-            try:
-                paragraph_index = int(paragraph.split(':')[0].replace('Paragraph ', '')) - 1
-                selected_paragraph = stored_paragraphs_1[paragraph_index]
-                summary = summarize_text(selected_paragraph)
-                sentiment = text_to_sentiment(selected_paragraph)
-                fin_spans = fin_ext(selected_paragraph)
-                return selected_paragraph, summary, sentiment, fin_spans
-            except (IndexError, ValueError):
-                return "Invalid selection", "Error", "Error", []
-
-        summarize_btn1.click(fn=lambda p: process_paragraph_1(p)[1], inputs=paragraph_1_dropdown, outputs=summary_textbox_1)
-        sentiment_btn1.click(fn=lambda p: process_paragraph_1(p)[2], inputs=paragraph_1_dropdown, outputs=sentiment_textbox_1)
-        analyze_btn1 = gr.Button("Analyze Financial Tone and FLS")
-        analyze_btn1.click(fn=lambda p: process_paragraph_1(p)[3], inputs=paragraph_1_dropdown, outputs=fin_spans_1)
-
-    # Process the selected paragraph from PDF 2
-    with gr.Column():
-        gr.Markdown("### PDF 2 Analysis")
-        selected_paragraph_2 = gr.Textbox(label="Selected Paragraph 2 Content", lines=4)
-        summarize_btn2 = gr.Button("Summarize Text from PDF 2")
-        sentiment_btn2 = gr.Button("Classify Financial Tone from PDF 2")
-        summary_textbox_2 = gr.Textbox(label="Summary for PDF 2", lines=4)
-        sentiment_textbox_2 = gr.Textbox(label="Classification for PDF 2", lines=4)
-        fin_spans_2 = gr.HighlightedText(label="Financial Tone Analysis for PDF 2")
-
-        def process_paragraph_2(paragraph):
-            try:
-                paragraph_index = int(paragraph.split(':')[0].replace('Paragraph ', '')) - 1
-                selected_paragraph = stored_paragraphs_2[paragraph_index]
-                summary = summarize_text(selected_paragraph)
-                sentiment = text_to_sentiment(selected_paragraph)
-                fin_spans = fin_ext(selected_paragraph)
-                return selected_paragraph, summary, sentiment, fin_spans
-            except (IndexError, ValueError):
-                return "Invalid selection", "Error", "Error", []
-
-        summarize_btn2.click(fn=lambda p: process_paragraph_2(p)[1], inputs=paragraph_2_dropdown, outputs=summary_textbox_2)
-        sentiment_btn2.click(fn=lambda p: process_paragraph_2(p)[2], inputs=paragraph_2_dropdown, outputs=sentiment_textbox_2)
-        analyze_btn2 = gr.Button("Analyze Financial Tone and FLS")
-        analyze_btn2.click(fn=lambda p: process_paragraph_2(p)[3], inputs=paragraph_2_dropdown, outputs=fin_spans_2)
-
+    with gr.Row():
+        # Process the selected paragraph from PDF 1
+        with gr.Column():
+            gr.Markdown("### PDF 1 Analysis")
+            selected_paragraph_1 = gr.Textbox(label="Selected Paragraph 1 Content", lines=4)
+            summarize_btn1 = gr.Button("Summarize Text from PDF 1")
+            sentiment_btn1 = gr.Button("Classify Financial Tone from PDF 1")
+            summary_textbox_1 = gr.Textbox(label="Summary for PDF 1", lines=4)
+            sentiment_textbox_1 = gr.Textbox(label="Classification for PDF 1", lines=4)
+            fin_spans_1 = gr.HighlightedText(label="Financial Tone Analysis for PDF 1")
+
+            def process_paragraph_1(paragraph):
+                try:
+                    paragraph_index = int(paragraph.split(':')[0].replace('Paragraph ', '')) - 1
+                    selected_paragraph = stored_paragraphs_1[paragraph_index]
+                    summary = summarize_text(selected_paragraph)
+                    sentiment = text_to_sentiment(selected_paragraph)
+                    fin_spans = fin_ext(selected_paragraph)
+                    return selected_paragraph, summary, sentiment, fin_spans
+                except (IndexError, ValueError):
+                    return "Invalid selection", "Error", "Error", []
+
+            summarize_btn1.click(fn=lambda p: process_paragraph_1(p)[1], inputs=paragraph_1_dropdown, outputs=summary_textbox_1)
+            sentiment_btn1.click(fn=lambda p: process_paragraph_1(p)[2], inputs=paragraph_1_dropdown, outputs=sentiment_textbox_1)
+            analyze_btn1 = gr.Button("Analyze Financial Tone and FLS")
+            analyze_btn1.click(fn=lambda p: process_paragraph_1(p)[3], inputs=paragraph_1_dropdown, outputs=fin_spans_1)
+
+        # Process the selected paragraph from PDF 2
+        with gr.Column():
+            gr.Markdown("### PDF 2 Analysis")
+            selected_paragraph_2 = gr.Textbox(label="Selected Paragraph 2 Content", lines=4)
+            summarize_btn2 = gr.Button("Summarize Text from PDF 2")
+            sentiment_btn2 = gr.Button("Classify Financial Tone from PDF 2")
+            summary_textbox_2 = gr.Textbox(label="Summary for PDF 2", lines=4)
+            sentiment_textbox_2 = gr.Textbox(label="Classification for PDF 2", lines=4)
+            fin_spans_2 = gr.HighlightedText(label="Financial Tone Analysis for PDF 2")
+
+            def process_paragraph_2(paragraph):
+                try:
+                    paragraph_index = int(paragraph.split(':')[0].replace('Paragraph ', '')) - 1
+                    selected_paragraph = stored_paragraphs_2[paragraph_index]
+                    summary = summarize_text(selected_paragraph)
+                    sentiment = text_to_sentiment(selected_paragraph)
+                    fin_spans = fin_ext(selected_paragraph)
+                    return selected_paragraph, summary, sentiment, fin_spans
+                except (IndexError, ValueError):
+                    return "Invalid selection", "Error", "Error", []
+
+            summarize_btn2.click(fn=lambda p: process_paragraph_2(p)[1], inputs=paragraph_2_dropdown, outputs=summary_textbox_2)
+            sentiment_btn2.click(fn=lambda p: process_paragraph_2(p)[2], inputs=paragraph_2_dropdown, outputs=sentiment_textbox_2)
+            analyze_btn2 = gr.Button("Analyze Financial Tone and FLS")
+            analyze_btn2.click(fn=lambda p: process_paragraph_2(p)[3], inputs=paragraph_2_dropdown, outputs=fin_spans_2)
 
 demo.launch()