app.py

import os
import gradio as gr
from transformers import pipeline
import spacy
import lib.read_pdf
import pandas as pd
import re
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import io
# 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')
fin_model_bis = pipeline("sentiment-analysis", model='ProsusAI/finbert', tokenizer='ProsusAI/finbert')

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 fin_ext_bis(text):
    results = fin_model_bis(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')]

def show(name):
    return f"{name}"

def get_excel_files(folder):
    return [f for f in os.listdir(folder) if f.endswith('.xlsx')]

def get_sheet_names(file):
    xls = pd.ExcelFile(os.path.join(PDF_FOLDER, file))
    return gr.update(choices=xls.sheet_names)

def process_and_compare(file1, sheet1, file2, sheet2):
    def process_file(file_path, sheet_name):
        # Extract year from file name
        year = int(re.search(r'(\d{4})', file_path).group(1))
        
        # Load the Excel file
        df = pd.read_excel(os.path.join(PDF_FOLDER, file_path), sheet_name=sheet_name, index_col=0)
        
        # Define expected columns based on extracted year
        historical_col = f'Historical {year - 1}'
        baseline_cols = [f'Baseline {year}', f'Baseline {year + 1}', f'Baseline {year + 2}']
        adverse_cols = [f'Adverse {year}', f'Adverse {year + 1}', f'Adverse {year + 2}']
        level_deviation_col = f'Level Deviation {year + 2}'

        # Drop rows and reset index
        df = df.iloc[4:].reset_index(drop=True)

        # Define the new column names
        new_columns = ['Country', 'Code', historical_col] + baseline_cols + adverse_cols + ['Adverse Cumulative', 'Adverse Minimum', level_deviation_col]
        
        # Ensure the number of columns matches
        if len(df.columns) == len(new_columns):
            df.columns = new_columns
        else:
            raise ValueError(f"Expected {len(new_columns)} columns, but found {len(df.columns)} columns in the data.")
        
        return df

    # Process both files
    df1 = process_file(file1, sheet1)
    df2 = process_file(file2, sheet2)
    year1 = int(re.search(r'(\d{4})', file1).group(1))
    year2 = int(re.search(r'(\d{4})', file2).group(1))
    # Calculate the differences
    # historical_col1 = f'Historical {int(year1) - 1}'
    # historical_col2 = f'Historical {int(year2) - 1}'
    
    # df1['Historical vs Adverse'] = df1[historical_col1] - df1['Adverse Cumulative']
    # df2['Historical vs Adverse'] = df2[historical_col2] - df2['Adverse Cumulative']

    # Merge dataframes on 'Country'
    merged_df = pd.merge(df2, df1, on='Country', suffixes=(f'_{year1}', f'_{year2}'))
    merged_df['Difference adverse cumulative growth'] = merged_df[f'Adverse Cumulative_{year2}'] - merged_df[f'Adverse Cumulative_{year1}']
    # Ensure data types are correct
    merged_df['Country'] = merged_df['Country'].astype(str)
    merged_df['Difference adverse cumulative growth'] = pd.to_numeric(merged_df['Difference adverse cumulative growth'], errors='coerce')

    # Create histogram plot with color coding
    fig, ax = plt.subplots(figsize=(12, 8))
    colors = plt.get_cmap('tab20').colors  # Use a colormap with multiple colors
    num_countries = len(merged_df['Country'])
    
    bars = ax.bar(merged_df['Country'], merged_df['Difference adverse cumulative growth'], color=colors[:num_countries])
    
    # Add a legend
    handles = [patches.Patch(color=color, label=country) for color, country in zip(colors[:num_countries], merged_df['Country'])]
    ax.legend(handles=handles, title='Countries', bbox_to_anchor=(1.05, 1), loc='upper left')
    
    ax.set_title(f'Histogram of Difference between Adverse cumulative growth of {year2} and {year1} for {sheet1}')
    ax.set_xlabel('Country')
    ax.set_ylabel('Difference')
    plt.xticks(rotation=90)

    # Save plot to a file
    file_path = 'output/plot.png'
    plt.savefig(file_path, format='png', bbox_inches='tight')
    plt.close()

    return file_path


stored_paragraphs_1 = []
stored_paragraphs_2 = []

with gr.Blocks() as demo:
    with gr.Tab("Financial Report Text Analysis"):
        gr.Markdown("## Financial Report Paragraph Selection and Analysis on adverse macro-economy scenario")

        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)
                selected_paragraph_1.change(show, paragraph_1_dropdown, selected_paragraph_1)
                summarize_btn1 = gr.Button("Summarize Text from PDF 1")
                summary_textbox_1 = gr.Textbox(label="Summary for PDF 1", lines=2)
                summarize_btn1.click(fn=lambda p: process_paragraph_1_sum(p), inputs=paragraph_1_dropdown, outputs=summary_textbox_1)
                sentiment_btn1 = gr.Button("Classify Financial Tone from PDF 1")
                sentiment_textbox_1 = gr.Textbox(label="Classification for PDF 1", lines=1)
                sentiment_btn1.click(fn=lambda p: process_paragraph_1_sent(p), inputs=paragraph_1_dropdown, outputs=sentiment_textbox_1)
                analyze_btn1 = gr.Button("Analyze Financial Tone on each sentence with yiyanghkust/finbert-tone")
                fin_spans_1 = gr.HighlightedText(label="Financial Tone Analysis for PDF 1")
                analyze_btn1.click(fn=lambda p: process_paragraph_1_sent_tone(p), inputs=paragraph_1_dropdown, outputs=fin_spans_1)
                analyze_btn1_ = gr.Button("Analyze Financial Tone on each sentence with ProsusAI/finbert")
                fin_spans_1_ = gr.HighlightedText(label="Financial Tone Analysis for PDF 1 bis")
                analyze_btn1_.click(fn=lambda p: process_paragraph_1_sent_tone_bis(p), 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)
                selected_paragraph_2.change(show, paragraph_2_dropdown, selected_paragraph_2)
                summarize_btn2 = gr.Button("Summarize Text from PDF 2")
                summary_textbox_2 = gr.Textbox(label="Summary for PDF 2", lines=2)
                summarize_btn2.click(fn=lambda p: process_paragraph_2_sum(p), inputs=paragraph_2_dropdown, outputs=summary_textbox_2)
                sentiment_btn2 = gr.Button("Classify Financial Tone from PDF 2")
                sentiment_textbox_2 = gr.Textbox(label="Classification for PDF 2", lines=1)
                sentiment_btn2.click(fn=lambda p: process_paragraph_2_sent(p), inputs=paragraph_2_dropdown, outputs=sentiment_textbox_2)
                analyze_btn2 = gr.Button("Analyze Financial Tone on each sentence with yiyanghkust/finbert-tone")
                fin_spans_2 = gr.HighlightedText(label="Financial Tone Analysis for PDF 2")
                analyze_btn2.click(fn=lambda p: process_paragraph_2_sent_tone(p), inputs=paragraph_2_dropdown, outputs=fin_spans_2)
                analyze_btn2_ = gr.Button("Analyze Financial Tone on each sentence with ProsusAI/finbert")
                fin_spans_2_ = gr.HighlightedText(label="Financial Tone Analysis for PDF 2 bis")
                analyze_btn2_.click(fn=lambda p: process_paragraph_2_sent_tone_bis(p), inputs=paragraph_2_dropdown, outputs=fin_spans_2_)

    with gr.Tab("Financial Report Table Analysis"):
        # New tab content goes here
        gr.Markdown("## Excel Data Comparison")

        with gr.Row():
            with gr.Column():
                file1 = gr.Dropdown(choices=get_excel_files(PDF_FOLDER), label="Select Excel File 1")
                file2 = gr.Dropdown(choices=get_excel_files(PDF_FOLDER), label="Select Excel File 2")
                sheet = gr.Dropdown(choices=[], label="Select Sheet for File 1 and 2")
            
            with gr.Column():
                result = gr.Image(label="Comparison pLot")
        
        def update_sheets(file):
            return get_sheet_names(file)
        
        file1.change(fn=update_sheets, inputs=file1, outputs=sheet)
        
        b1 = gr.Button("Compare Data")
        b1.click(fn=process_and_compare, inputs=[file1, sheet, file2, sheet], outputs=result)

demo.launch()