Create app.py

app.py

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+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()