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main.py

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+from transformers import RobertaForSequenceClassification, AutoTokenizer, AutoModelForTokenClassification, 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("wonrax/phobert-base-vietnamese-sentiment")
+senti_tokenizer = AutoTokenizer.from_pretrained("wonrax/phobert-base-vietnamese-sentiment", use_fast=False)
+
+# Load the model and tokenizer segmentation
+seg_tokenizer = AutoTokenizer.from_pretrained("NlpHUST/vi-word-segmentation")
+seg_model = AutoModelForTokenClassification.from_pretrained("NlpHUST/vi-word-segmentation")
+nlp = pipeline("token-classification", model=seg_model, tokenizer=seg_tokenizer)
+
+
+# Word segmented
+def segmentation(sentences):
+    segmented_sentences = []
+    for sentence in sentences:
+        ner_results = nlp(sentence)
+        sentence_tok = ""
+        for e in ner_results:
+            if "##" in e["word"]:
+                sentence_tok = sentence_tok + e["word"].replace("##", "")
+            elif e["entity"] == "I":
+                sentence_tok = sentence_tok + "_" + e["word"]
+            else:
+                sentence_tok = sentence_tok + " " + e["word"]
+        segmented_sentences.append(sentence_tok.strip())
+    return segmented_sentences
+
+
+# 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)
+    segmented_sentences = segmentation(sentences)
+    
+    # Analyze the sentiment of each sentence
+    results = []
+    for sentence in segmented_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', 'Positive', 'Neutral'],
+                           'Score': [neg_avg, pos_avg, neu_avg]})
+
+    # Set custom colors for the pie chart
+    colors = ['#BDBDBD', '#9ACD32', '#87CEFA']
+
+    # 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', 'Positive', 'Neutral', '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', 'Positive', 'Neutral']
+        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', 'Positive', 'Neutral'])
+    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):
+    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
+        segmented_sentence = segmentation([sentence])
+        results = analyze(segmented_sentence[0])
+
+        # Get the label names
+        label_names = ['Negative', 'Positive', 'Neutral']
+
+        # 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, output_text
+    
+    elif sentence:
+        # Only sentence input is provided
+        # Analyze the sentiment of the input sentence
+        segmented_sentence = segmentation([sentence])
+        results = analyze(segmented_sentence[0])
+
+        # Get the label names
+        label_names = ['Negative', 'Positive', 'Neutral']
+
+        # 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 None, None, output_text
+    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, None
+
+inputs = [
+    gr.inputs.File(label="Chọn Tệp Bạn Muốn Phân Tích"),
+    gr.inputs.Textbox(label="Nhập Văn Bản")
+]
+outputs = [
+    gr.outputs.File(label="Kết Quả Phân Tích Excel"),
+    gr.outputs.Image(type="filepath", label="Thông Số Phân Tích"),
+    gr.outputs.Textbox(label="Kết Quả Phân Tích")
+]
+
+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)

requirements.txt

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+transformers
+torch
+nltk
+python-docx
+pandas
+matplotlib
+openpyxl
+gradio