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# Use a pipeline as a high-level helper
from transformers import pipeline
import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
analyser = pipeline("text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
# model_path = ("./Models/models--distilbert--distilbert-base-uncased-finetuned-sst-2-english/snapshots/714eb0fa89d2f80546fda750413ed43d93601a13")
# analyser = pipeline("text-classification", model=model_path)
# print(analyser(["This product is good!", "This product is expensive!"]))
def sentiment_analysis(text_to_review):
sentiment = analyser(text_to_review)
return sentiment[0]['label']
# print(sentiment_analysis(["This product is good!", "This product is expensive!"]))
def plot_sentiment_pie(df):
# Count the number of positive and negative reviews
sentiment_counts = df['Sentiment'].value_counts()
# Create the pie chart
fig, ax = plt.subplots(figsize=(6, 6))
ax.pie(sentiment_counts.values, labels=sentiment_counts.index, autopct='%1.1f%%')
ax.set_title('Sentiment Distribution')
# Convert the Matplotlib figure to a Gradio Plots component
return fig
def read_excel_and_get_sentiment(file):
try:
df = pd.read_excel(file)
if 'Review' not in df.columns:
raise KeyError("'Review' column not found in the Excel file.")
df['Sentiment'] = df['Review'].apply(sentiment_analysis)
chart_object = plot_sentiment_pie(df)
return df, chart_object
except FileNotFoundError:
print(f"Error: {file} not found.")
raise
except Exception as e:
print(f"Error: {e}")
raise
gr.close_all()
demo = gr.Interface(fn=read_excel_and_get_sentiment,
inputs=[gr.File(file_types= ['xlsx'],label="upload your review comment excel file.")],
outputs=[gr.DataFrame(label="Reviewed text"), gr.Plot(label="Sentiment Analysis")],
title="@IT AI Enthusiast (https://www.youtube.com/@itaienthusiast/) - Sentiment Analysis",
description="THIS APPLICATION WILL BE USED TO ANALYZER THE SENTIMENT BASED ON THE COMMENT PROVIDER.",
theme=gr.themes.Soft(),
concurrency_limit=16)
demo.launch()
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