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import pandas as pd
import matplotlib.pyplot as plt
from transformers import pipeline
import gradio as gr
import os
# Define the default file path for the example reviews.txt
DEFAULT_FILE_PATH = "reviews.txt"
# Initialize the sentiment analyzer pipeline
sentiment_Analyzer = pipeline("text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
# Function to read reviews from a text file and convert to a pandas DataFrame
def read_reviews_to_dataframe(file_path):
reviews = []
with open(file_path, 'r', encoding='utf-8') as file:
for line in file:
reviews.append(line.strip())
df = pd.DataFrame(reviews, columns=['Review'])
return df
# Analyzer function to apply sentiment analysis on each review
def analyzer(text):
output = sentiment_Analyzer(text)[0]
return output['label'], output['score']
# Function to add an 'Evaluation' column to the DataFrame based on sentiment analysis
def evaluate_reviews(df):
df['Evaluation'] = df['Review'].apply(lambda x: analyzer(x))
df[['Sentiment', 'Score']] = pd.DataFrame(df['Evaluation'].tolist(), index=df.index)
df.drop(columns=['Evaluation'], inplace=True)
return df
# Function to create a pie chart showing the percentage of positive and negative reviews
def create_pie_chart(df):
sentiment_counts = df['Sentiment'].value_counts()
labels = sentiment_counts.index
sizes = sentiment_counts.values
colors = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99']
fig, ax = plt.subplots()
ax.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90)
ax.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.title('Sentiment Analysis of Reviews')
chart_path = 'sentiment_pie_chart.png'
plt.savefig(chart_path)
plt.close(fig)
return chart_path
# Function to process the file
def process_reviews(file):
file_path = file.name
df = read_reviews_to_dataframe(file_path)
df = evaluate_reviews(df)
chart_path = create_pie_chart(df)
return df, chart_path
# Gradio interface function
def gradio_interface(file):
return process_reviews(file)
# Create the Gradio interface
interface = gr.Interface(
fn=gradio_interface,
inputs=gr.File(label="Upload a text file with reviews"),
outputs=[
gr.Dataframe(label="Reviews with Evaluation"),
gr.Image(label="Sentiment Pie Chart")
],
title="Sentiment Analyzer",
description="Upload a text file with reviews to analyze the sentiment and visualize the results.",
examples=[[DEFAULT_FILE_PATH]],
allow_flagging="never"
)
# Launch the Gradio interface
interface.launch()
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