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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 | |
| # model_path = ("../.venv/Modal/models--distilbert--distilbert-base-uncased-finetuned-sst-2-english" | |
| # "/snapshots/714eb0fa89d2f80546fda750413ed43d93601a13") | |
| analyzer = pipeline("text-classification", | |
| model="distilbert/distilbert-base-uncased-finetuned-sst-2-english") | |
| # analyzer = pipeline("text-classification", | |
| # model=model_path) | |
| # print(analyzer(["This product is good", "This product was quite expensive"])) | |
| 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('Review Sentiment Counts') | |
| ax.set_xlabel('Sentiment') | |
| ax.set_ylabel('Count') | |
| # ax.set_xticklabels(['Positive', 'Negative'], rotation=0) | |
| # Return the figure object | |
| return fig | |
| def read_reviews_and_analyze_sentiment(file_object): | |
| df = pd.read_excel(file_object) | |
| if 'Reviews' not in df.columns: | |
| raise ValueError("Error file must contains a 'Reviews' column.") | |
| df['Sentiment'] = df['Reviews'].apply(sentiment_analyzer) | |
| chart_object = sentiment_bar_chart(df) | |
| return df, chart_object | |
| # result = read_reviews_and_analyze_sentiment("../.venv/Files/reviews-sentiment.xlsx") | |
| # print(result) | |
| demo = gr.Interface(fn=read_reviews_and_analyze_sentiment, | |
| inputs=[gr.File(file_types=[".xlsx"], label="Input your review comment file")], | |
| outputs=[gr.Dataframe(label="Sentiment"), gr.Plot(label="Sentiment Analysis")], | |
| title="Project 3: Sentiment Analyzer", | |
| description="THIS APPLICATIONS WILL BE USED TO ANALYZER THE SENTIMENT BASED ON FILE UPLOADED.") | |
| demo.launch() |