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