Create main.py

main.py

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+import torch
+import gradio as gr
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from transformers import pipeline
+
+analyis = pipeline(
+    "text-classification", 
+    model="distilbert/distilbert-base-uncased-finetuned-sst-2-english"
+)
+
+def sentiment_analysis(review):
+    sentiment = analyis(review)
+    return sentiment[0]['label']
+
+def sentiment_bar_chart(df):
+    sentiment_counts = df['Sentiment'].value_counts()
+
+    fig, ax = plt.subplots()
+    sentiment_counts.plot(kind='pie', ax=ax, autopct='%1.1f%%', color=['green', 'red'])
+    ax.set_title('Review Sentiment Counts')
+    ax.set_xlabel('Sentiment')
+    ax.set_ylabel('Count')
+
+    return fig
+
+def read_reviews_and_analyze_sentiment(file_object):
+    df = pd.read_excel(file_object)
+
+    if 'Reviews' not in df.columns:
+        raise ValueError("Excel file must contain a 'Review' column")
+
+    df['Sentiment'] = df['Reviews'].apply(sentiment_analysis)
+    chart_object = sentiment_bar_chart(df)
+    return df, chart_object
+
+demo = gr.Interface(
+    fn=sentiment_analysis,
+    inputs=[gr.File(file_types=["xlsx"], label="Input users reviews")],
+    outputs=[gr.DataFrame(label="User sentiment"), gr.Plot(label="Users' Sentiments")],
+    title="@caesar-2series: Users' Sentiment Analysis",
+    description="User Sentiment Review Analysis Based on File Uploaded"
+)
+
+demo.launch()