from transformers import RobertaForSequenceClassification, AutoTokenizer, pipeline import torch import nltk import docx2txt import pandas as pd import os import matplotlib.pyplot as plt import openpyxl from openpyxl.styles import Font, Color, PatternFill from openpyxl.styles.colors import WHITE import gradio as gr nltk.download('punkt') # Load the model and tokenizer senti_model = RobertaForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest") senti_tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest", use_fast=False) # File read def read_file(docx): try: text = docx2txt.process(docx) lines = text.split('\n') lines = [line.strip() for line in lines] lines = [line for line in lines if line] return lines # add this line except Exception as e: print(f"Error reading file: {e}") # Define a function to analyze the sentiment of a text def analyze(sentence): input_ids = torch.tensor([senti_tokenizer.encode(sentence)]) with torch.no_grad(): out = senti_model(input_ids) results = out.logits.softmax(dim=-1).tolist() return results[0] def file_analysis(docx): # Read the file and segment the sentences sentences = read_file(docx) # Analyze the sentiment of each sentence results = [] for sentence in sentences: results.append(analyze(sentence)) return results def generate_pie_chart(df): # Calculate the average scores neg_avg = df['Negative'].mean() pos_avg = df['Positive'].mean() neu_avg = df['Neutral'].mean() # Create a new DataFrame with the average scores avg_df = pd.DataFrame({'Sentiment': ['Negative', 'Neutral', 'Positive'], 'Score': [neg_avg, neu_avg, pos_avg]}) # Set custom colors for the pie chart colors = ['#BDBDBD', '#87CEFA', '#9ACD32'] # Create a pie chart showing the average scores plt.pie(avg_df['Score'], labels=avg_df['Sentiment'], colors=colors, autopct='%1.1f%%') plt.title('Average Scores by Sentiment') # Save the pie chart as an image file in the static folder pie_chart_name = 'pie_chart.png' plt.savefig(pie_chart_name) plt.close() return pie_chart_name def generate_excel_file(df): # Create a new workbook and worksheet wb = openpyxl.Workbook() ws = wb.active # Add column headers to the worksheet headers = ['Negative', 'Neutral', 'Positive', 'Text'] for col_num, header in enumerate(headers, 1): cell = ws.cell(row=1, column=col_num) cell.value = header cell.font = Font(bold=True) # Set up cell formatting for each sentiment fill_dict = { 'Negative': PatternFill(start_color='BDBDBD', end_color='BDBDBD', fill_type='solid'), 'Positive': PatternFill(start_color='9ACD32', end_color='9ACD32', fill_type='solid'), 'Neutral': PatternFill(start_color='87CEFA', end_color='87CEFA', fill_type='solid') } # Loop through each row of the input DataFrame and write data to the worksheet for row_num, row_data in df.iterrows(): # Calculate the highest score and corresponding sentiment for this row sentiment_cols = ['Negative', 'Neutral', 'Positive'] scores = [row_data[col] for col in sentiment_cols] max_score = max(scores) max_index = scores.index(max_score) sentiment = sentiment_cols[max_index] # Write the data to the worksheet for col_num, col_data in enumerate(row_data, 1): cell = ws.cell(row=row_num + 2, column=col_num) cell.value = col_data if col_num in [1, 2, 3]: if col_data == max_score: cell.fill = fill_dict[sentiment] if col_num == 4: fill = fill_dict[sentiment] font_color = WHITE if fill.start_color.rgb == 'BDBDBD' else Color('000000') cell.fill = fill cell.font = Font(color=font_color) if col_data == max_score: cell.fill = fill_dict[sentiment] # Save the workbook excel_file_path = 'result.xlsx' wb.save(excel_file_path) return excel_file_path def process_file(docx): # Perform analysis on the file results = file_analysis(docx) # Create a DataFrame from the results df = pd.DataFrame(results, columns=['Negative', 'Neutral', 'Positive']) df['Text'] = read_file(docx) # Generate the pie chart and excel file pie_chart_name = generate_pie_chart(df) excel_file_path = generate_excel_file(df) return pie_chart_name, excel_file_path def analyze_file(file, sentence): excel_file_path = None pie_chart_name = None if file and sentence: # Both file and sentence inputs are provided # Process the uploaded file and generate the output files pie_chart_name, excel_file_path = process_file(file.name) # Analyze the sentiment of the input sentence results = analyze(sentence) # Get the label names label_names = ['Negative', 'Neutral', 'Positive'] # Create the output text with labels and scores output_text = "" for label, score in zip(label_names, results): score_formatted = "{:.2f}".format(score) output_text += f"{label}: {score_formatted}\n" return excel_file_path, pie_chart_name elif sentence: # Only sentence input is provided # Analyze the sentiment of the input sentence results = analyze(sentence) # Get the label names label_names = ['Negative', 'Neutral', 'Positive'] # Create the output text with labels and scores output_text = "" for label, score in zip(label_names, results): score_formatted = "{:.2f}".format(score) output_text += f"{label}: {score_formatted}\n" # Generate the pie chart and excel file pie_chart_name = generate_pie_chart(pd.DataFrame([results], columns=['Negative', 'Neutral', 'Positive'])) excel_file_path = generate_excel_file(pd.DataFrame([results], columns=['Negative', 'Neutral', 'Positive'])) return excel_file_path, pie_chart_name elif file: # Only file input is provided # Process the uploaded file and generate the output files pie_chart_name, excel_file_path = process_file(file.name) # Return the file paths for the pie chart and excel file return excel_file_path, pie_chart_name inputs = [ gr.inputs.File(label="Please Select World (Docx) File for Analysis"), gr.inputs.Textbox(label="Or Enter Text to try now") ] outputs = [ gr.outputs.File(label="Analysis Result Excel"), gr.outputs.Image(type="filepath", label="Analysis Metrics"), ] interface = gr.Interface( fn=analyze_file, inputs=inputs, outputs=outputs, title="Sentiment Analysis", allow_flagging="never" # Disable flag button ) if __name__ == "__main__": interface.launch()