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Create main.py
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main.py
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import torch
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import gradio as gr
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import pandas as pd
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import matplotlib.pyplot as plt
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from transformers import pipeline
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analyis = pipeline(
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"text-classification",
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model="distilbert/distilbert-base-uncased-finetuned-sst-2-english"
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)
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def sentiment_analysis(review):
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sentiment = analyis(review)
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return sentiment[0]['label']
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def sentiment_bar_chart(df):
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sentiment_counts = df['Sentiment'].value_counts()
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fig, ax = plt.subplots()
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sentiment_counts.plot(kind='pie', ax=ax, autopct='%1.1f%%', color=['green', 'red'])
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ax.set_title('Review Sentiment Counts')
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ax.set_xlabel('Sentiment')
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ax.set_ylabel('Count')
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return fig
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def read_reviews_and_analyze_sentiment(file_object):
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df = pd.read_excel(file_object)
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if 'Reviews' not in df.columns:
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raise ValueError("Excel file must contain a 'Review' column")
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df['Sentiment'] = df['Reviews'].apply(sentiment_analysis)
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chart_object = sentiment_bar_chart(df)
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return df, chart_object
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demo = gr.Interface(
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fn=sentiment_analysis,
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inputs=[gr.File(file_types=["xlsx"], label="Input users reviews")],
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outputs=[gr.DataFrame(label="User sentiment"), gr.Plot(label="Users' Sentiments")],
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title="@caesar-2series: Users' Sentiment Analysis",
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description="User Sentiment Review Analysis Based on File Uploaded"
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)
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demo.launch()
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