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import os
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')
def split_in_sentences(text):
doc = nlp(text)
return [str(sent).strip() for sent in doc.sents]
def make_spans(text, results):
results_list = [res['label'] for res in results]
facts_spans = list(zip(split_in_sentences(text), results_list))
return facts_spans
# Initialize pipelines
summarizer = pipeline("summarization", model="human-centered-summarization/financial-summarization-pegasus")
fin_model = pipeline("sentiment-analysis", model='yiyanghkust/finbert-tone', tokenizer='yiyanghkust/finbert-tone')
def summarize_text(text):
resp = summarizer(text)
return resp[0]['summary_text']
def text_to_sentiment(text):
sentiment = fin_model(text)[0]["label"]
return sentiment
def fin_ext(text):
results = fin_model(split_in_sentences(text))
return make_spans(text, results)
def extract_and_summarize(pdf1, pdf2):
if not pdf1 or not pdf2:
return [], []
pdf1_path = os.path.join(PDF_FOLDER, pdf1)
pdf2_path = os.path.join(PDF_FOLDER, pdf2)
# Extract and format paragraphs
paragraphs_1 = lib.read_pdf.extract_and_format_paragraphs(pdf1_path)
paragraphs_2 = lib.read_pdf.extract_and_format_paragraphs(pdf2_path)
start_keyword = "Main risks to"
end_keywords = ["4. Appendix", "Annex:", "4. Annex", "Detailed tables", "ACKNOWLEDGEMENTS", "STATISTICAL ANNEX", "PROSPECTS BY MEMBER STATES"]
start_index1, end_index1 = lib.read_pdf.find_text_range(paragraphs_1, start_keyword, end_keywords)
start_index2, end_index2 = lib.read_pdf.find_text_range(paragraphs_2, start_keyword, end_keywords)
paragraphs_1 = lib.read_pdf.extract_relevant_text(paragraphs_1, start_index1, end_index1)
paragraphs_2 = lib.read_pdf.extract_relevant_text(paragraphs_2, start_index2, end_index2)
paragraphs_1 = lib.read_pdf.split_text_into_paragraphs(paragraphs_1, 0)
paragraphs_2 = lib.read_pdf.split_text_into_paragraphs(paragraphs_2, 0)
return paragraphs_1, paragraphs_2
# Gradio interface setup
PDF_FOLDER = "data"
def get_pdf_files(folder):
return [f for f in os.listdir(folder) if f.endswith('.pdf')]
stored_paragraphs_1 = []
stored_paragraphs_2 = []
with gr.Blocks() as demo:
gr.Markdown("## Financial Report Paragraph Selection and Analysis")
with gr.Row():
# Upload PDFs
with gr.Column():
pdf1 = gr.Dropdown(choices=get_pdf_files(PDF_FOLDER), label="Select PDF 1")
pdf2 = gr.Dropdown(choices=get_pdf_files(PDF_FOLDER), label="Select PDF 2")
with gr.Column():
b1 = gr.Button("Extract and Display Paragraphs")
paragraph_1_dropdown = gr.Dropdown(label="Select Paragraph from PDF 1")
paragraph_2_dropdown = gr.Dropdown(label="Select Paragraph from PDF 2")
def update_paragraphs(pdf1, pdf2):
global stored_paragraphs_1, stored_paragraphs_2
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)
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)
demo.launch()
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