Delete app.py

app.py

@@ -1,213 +0,0 @@
-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="Select File for Analysis"),
-    gr.inputs.Textbox(label="Enter Text")
-]
-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(share=True)