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| import torch | |
| import gradio as gr | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| # Use a pipeline as a high-level helper | |
| from transformers import pipeline | |
| #analyzer = pipeline("text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english") | |
| analyzer = pipeline("text-classification", model="lxyuan/distilbert-base-multilingual-cased-sentiments-student") | |
| def sentiment_analyzer(review): | |
| sentiment = analyzer(review) | |
| return sentiment[0]['label'] | |
| def sentiment_bar_chart(df): | |
| sentiment_counts = df['Sentiment'].value_counts() | |
| # Create a bar chart | |
| fig, ax = plt.subplots() | |
| sentiment_counts.plot(kind='pie', ax=ax, autopct='%1.1f%%', color=['green', 'red']) | |
| ax.set_title('Reviews') | |
| ax.set_xlabel('Stimmung') | |
| ax.set_ylabel('Anzahl') | |
| # ax.set_xticklabels(['Positive', 'Negative'], rotation=0) | |
| # Return the figure object | |
| return fig | |
| def read_reviews_and_analyze_sentiment(file_object): | |
| # Load the Excel file into a DataFrame | |
| df = pd.read_excel(file_object) | |
| print(df.columns) | |
| # Check if 'Review' column is in the DataFrame | |
| #for col in df.columns: | |
| #print(f"col={col}") | |
| if 'Reviews' not in df.columns: | |
| raise ValueError("Die Excel-Datei muss eine Spalte 'Reviews' enthalten.") | |
| # Apply the get_sentiment function to each review in the DataFrame | |
| df['Sentiment'] = df['Reviews'].apply(sentiment_analyzer) | |
| chart_object = sentiment_bar_chart(df) | |
| return chart_object, df | |
| demo = gr.Interface(fn=read_reviews_and_analyze_sentiment, | |
| inputs=[gr.File(file_types=["xlsx"], label="Laden Sie Ihre xls-Review-Datei hoch")], | |
| outputs=[ gr.Plot(label="Stimmungsanalyse"), gr.Dataframe(label="Stimmungen")], | |
| title="Project 3: Stimmung-Analysator", | |
| description="DIESE ANWENDUNG WIRD VERWENDET, UM DIE STIMMUNG AUF DER GRUNDLAGE DER HOCHGELADENEN DATEI ZU ANALYSIEREN.", | |
| allow_flagging="never", | |
| submit_btn="Übermitteln", | |
| clear_btn="Bereinigen",) | |
| demo.launch() | |