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 product is good", "This product was quite expensive"]))


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

def generate_sentiment_bar_chart(df):
    # Validate DataFrame
    if not {'Review', 'Sentiment'}.issubset(df.columns):
        raise ValueError("DataFrame must contain 'Review' and 'Sentiment' columns.")
    
    # Count occurrences of each sentiment
    sentiment_counts = df['Sentiment'].value_counts()
    
    # Create bar chart
    fig, ax = plt.subplots(figsize=(8, 5))
    sentiment_counts.plot(kind='bar', color=['green', 'red'], edgecolor='black', ax=ax)
    
    # Customize plot
    ax.set_title("Sentiment Distribution", fontsize=14)
    ax.set_xlabel("Sentiment", fontsize=12)
    ax.set_ylabel("Count", fontsize=12)
    ax.grid(axis='y', linestyle='--', alpha=0.7)
    plt.xticks(rotation=45)
    
    # Adjust layout
    plt.tight_layout()
    
    return fig


def read_review_and_analyze_sentiment(file_object):
    df = pd.read_excel(file_object)
    if 'Review' not in df.columns:
        raise ValueError("Excel file must contain a 'Review' colum.")
    df['Sentiment'] = df['Review'].apply(sentiment_analyzer)
    chat_object = generate_sentiment_bar_chart(df)
    return df, chat_object
# file = '../files/product_review.xlsx'
# result = read_review_and_analyze_sentiment(file)
# print(result)

gr.close_all()

# demo = gr.Interface(fn=summary, inputs="text", outputs="text")
demo = gr.Interface(fn=read_review_and_analyze_sentiment, 
                    inputs=[gr.File(file_types=[".xlsx"],label="Input your review comment")],
                    outputs=[gr.Dataframe(label="Sentiment"), gr.Plot(label="Sentiment Analysis")],
                    title="GenAI Project 3: Sentiment Analyzer",
                    description="This application is use to analyze the sentiment based on the File uploaded.")
demo.launch()