app.py

import torch
import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt

# Use a pipeline as a high-level helper
from transformers import pipeline
# model_path = ("../Models/models--distilbert--distilbert-base-uncased-finetuned-sst-2-english"
#               "/snapshots/714eb0fa89d2f80546fda750413ed43d93601a13")

# analyzer = pipeline("text-classification", model=model_path)


analyzer = pipeline("text-classification",
                model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")

# print(analyzer(["This production is good", "This product was quite expensive"]))


def sentiment_analyzer(review):
    sentiment = analyzer(review)
    return sentiment[0]['label']

def sentiment_pie_chart(df):
    sentiment_counts = df['Sentiment'].value_counts()    

    # Create a pie chart
    fig, ax = plt.subplots()
    sentiment_counts.plot(kind='pie', autopct='%1.1f%%', colors=['green', 'red'], ax=ax)

    # Remove the y-axis label
    ax.set_ylabel('')  

    ax.set_title('Review Sentiment Chart')
    return fig


def read_reviews_and_analyze_sentiment(file_object):
    # Load the Excel file into a DataFrame
    df = pd.read_excel(file_object)

    # Check if 'Review' column is in the DataFrame
    if 'Reviews' not in df.columns:
        raise ValueError("Excel file must contain a 'Reviews' column.")

    # Apply the get_sentiment function to each review in the DataFrame
    df['Sentiment'] = df['Reviews'].apply(sentiment_analyzer)
    df = df[['Sentiment'] + [col for col in df.columns if col != 'Sentiment']]
    
    chart_object = sentiment_pie_chart(df)
    return df, chart_object

# result = read_reviews_and_analyze_sentiment("../Files/Prod-review.xlsx")
# print(result)

demo = gr.Interface(fn=read_reviews_and_analyze_sentiment,
                    inputs=[gr.File(file_types=["xlsx"], label="Upload your reviews file")],
                    outputs=[gr.Dataframe(label="Sentiments"), gr.Plot(label="Sentiment Analysis")],
                    title="Sentiment Analyzer",
                    description="Analyze the sentiment based on reviews (Excel file .xlsx must contain a 'Reviews' column)")
demo.launch()