app.py

import pandas as pd
import matplotlib.pyplot as plt
from wordcloud import WordCloud
import seaborn as sns
import gradio as gr

def generate_wordcloud(text, title):
    wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)
    plt.figure(figsize=(10, 5))
    plt.imshow(wordcloud, interpolation='bilinear')
    plt.axis('off')
    plt.title(title)
    plt.show()

def generate_bar_plot(data, x, y, title):
    plt.figure(figsize=(10, 5))
    sns.barplot(x=x, y=y, data=data)
    plt.title(title)
    plt.show()

def generate_line_plot(data, x, y, title):
    plt.figure(figsize=(10, 5))
    sns.lineplot(x=x, y=y, data=data)
    plt.title(title)
    plt.show()

def sentiment_analysis(csv_file):
    # Load CSV file
    df = pd.read_csv(csv_file)

    # Assuming you have a 'Sentiment' column in your CSV indicating positive or negative sentiment

    # Positive Sentiment
    positive_df = df[df['Sentiment'] == 'positive']
    positive_text = ' '.join(positive_df['Content'])
    generate_wordcloud(positive_text, 'Positive Sentiment Word Cloud')
    generate_bar_plot(positive_df, 'Label', 'Count', 'Positive Sentiment Distribution')
    generate_line_plot(positive_df, 'Created At', 'SentimentScore', 'Positive Sentiment over Time')

    # Negative Sentiment
    negative_df = df[df['Sentiment'] == 'negative']
    negative_text = ' '.join(negative_df['Content'])
    generate_wordcloud(negative_text, 'Negative Sentiment Word Cloud')
    generate_bar_plot(negative_df, 'Label', 'Count', 'Negative Sentiment Distribution')
    generate_line_plot(negative_df, 'Created At', 'SentimentScore', 'Negative Sentiment over Time')

# Gradio Interface
csv_file_input = gr.inputs.File(label="Select CSV file")
interface = gr.Interface(fn=sentiment_analysis, inputs=csv_file_input, outputs=None, title="Sentiment Analysis", description="Generates word clouds, bar plots, and line plots based on sentiment of text data from CSV files")
interface.launch()