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from transformers import pipeline
import torch
import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
def sentiment_analysis(text):
analyzer = pipeline("text-classification", model="mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis")
# analyzer = pipeline("text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
sentiment = analyzer(text)
return [sentiment[0]['label'], sentiment[0]['score']]
# Test 1:
# print(analyzer(["This is awesome. Reliable product.", "Very expensive product. Company should use better pricing."]))
# print(sentiment_analysis(["This is awesome. Reliable product.", "Very expensive product. Company should use better pricing."]))
# [{'label': 'POSITIVE', 'score': 0.9998791217803955}, {'label': 'NEGATIVE', 'score': 0.9994811415672302}]
# Test with Gradio:
# gr.close_all()
# version 0.1
# demo = gr.Interface(
# fn=sentiment_analysis,
# inputs=[gr.Textbox(label="Text Input for Sentiment Analysis", lines=4)],
# outputs=[gr.Textbox(label="Analyzed Sentiment", lines=4), gr.Textbox(label="Sentiment Strength", lines=1)],
# title="GenAI Sentiment Analyzer",
# description="This App does seniment analysis of text input")
# demo.launch()
# Uploading an excel file and getting output as required:
def create_charts(df):
# Validate DataFrame
if not all(col in df.columns for col in ['Review', 'Sentiment', 'Sentiment Score']):
raise ValueError("The DataFrame must contain 'Review', 'Sentiment', and 'Sentiment Score' columns.")
# Create Pie Chart for Sentiment Distribution
sentiment_counts = df['Sentiment'].value_counts()
fig1, ax1 = plt.subplots(figsize=(8, 6))
ax1.pie(sentiment_counts, labels=sentiment_counts.index, autopct='%1.1f%%', colors=['skyblue', 'lightcoral', 'lightgreen'])
ax1.set_title('Distribution of Positive and Negative Reviews')
# Create Scatter Plot for Sentiment Scores
fig2, ax2 = plt.subplots(figsize=(10, 6))
for sentiment, color in zip(['positive', 'negative'], ['green', 'red']):
subset = df[df['Sentiment'].str.lower() == sentiment]
ax2.scatter(subset.index, subset['Sentiment Score'], label=sentiment.capitalize(), color=color, alpha=0.6)
ax2.axhline(0, color='gray', linewidth=0.5)
ax2.set_xlabel('Review Index')
ax2.set_ylabel('Sentiment Score')
ax2.set_title('Scatter Plot of Reviews by Sentiment Score')
ax2.legend()
return fig1, fig2
def analyze_reviews(file_path):
# Read the Excel file
df = pd.read_excel(file_path)
# Attempt to identify the review column if it is not labeled correctly
if 'Review' not in df.columns:
for col in df.columns:
if df[col].dtype == 'object': # Assuming reviews are text
df.rename(columns={col: 'Review'}, inplace=True)
break
# Ensure the dataframe now has a 'Review' column
if 'Review' not in df.columns:
raise ValueError("The input file must contain a column with review text.")
# Remove any column that contains serial numbers
df = df[[col for col in df.columns if not pd.api.types.is_numeric_dtype(df[col]) or col == 'Review']]
# Apply the get_sentiment function to each review
results = df['Review'].apply(sentiment_analysis)
# Split the results into separate columns for sentiment and sentiment score
[df['Sentiment'], df['Sentiment Score']] = zip(*results)
# Adjust the sentiment score to be negative if the sentiment is negative
df.loc[df['Sentiment'].str.lower() == 'negative' , 'Sentiment Score'] *= -1
pie_chart, scatter_plot = create_charts(df)
return [df, pie_chart, scatter_plot]
# Example usage
# file_path = '/teamspace/studios/this_studio/sentiment-analyzer/Sample_Sentiments (1).xlsx'
# result_df = analyze_reviews(file_path)
# print(result_df)
gr.close_all()
# version 0.2
demo = gr.Interface(
fn=analyze_reviews,
inputs=[gr.File(label="Upload your excel file containing user reviews")],
outputs=[
gr.DataFrame(label="Analysis of the uploaded excel file"),
gr.Plot(label="Sentiment Analysis - Positive & Negative"),
gr.Plot(label="Sentiment Analysis - Sentiment Score Distribution")
],
title="GenAI Sentiment Analyzer",
description="This App does sentiment analysis of User Reviews")
demo.launch()
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