app.py

import gradio as gr # Gradio library to create an interactive interface
from transformers import pipeline # Transformers libraries which imports pipeline to use Hugging-Face models
import pandas as pd # Pandas library for data manipulation and analysis
import matplotlib.pylab as plt # Matplot library for the interactive visualizations

# Initialize the analyzers

# Loads a pretrained model for the Arabic language
arabic_analyzer = pipeline('sentiment-analysis', model='CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment')

# Loads a pretrained model for the English language
english_analyzer = pipeline('sentiment-analysis', model='distilbert-base-uncased-finetuned-sst-2-english')


# Define function

def sentiment_analysis(language,file):
  # Select the appropriate analyzer
  if language == "Arabic":
    analyzer = arabic_analyzer
  else:
    analyzer = english_analyzer

  results = []

  # Read the sentences from the uploaded file
  with open (file.name,'r') as fn:
    sentences = fn.readlines()

  # Perform sentiment analysis on each sentence
  for sentence in sentences:
    result = analyzer(sentence)
    result = result[0]

    results.append({
        "Sentence": sentence,
        "Label": result['label'],
        "Score": f"{result['score'] *100:.2f}%"
    })
  # Convert the results into a DataFrame
  df = pd.DataFrame(results)
  # Ensure every label is lower, if not this will cause a logic error
  # English labels are Upper but Arabic labels are lower
  df['Label'] = df['Label'].str.lower()
  # Take the "Label" column values
  label_value = df['Label'].value_counts()

  # Pre-Define the plot parameters
  labels = ['Positive','Neutral','Negative']
  counts = [label_value.get('positive',0),
           label_value.get('neutral',0),
           label_value.get('negative',0)]
  colors=['green','gray','red']

  # Create a bar plot
  plt.bar(labels,counts,color=colors)
  plt.title('Sentiment-Analysis')
  plt.xlabel("Labels")
  plt.ylabel("No. of sentences")


  return df,plt


# Set up the Gradio interface

demo = gr.Interface(
    fn=sentiment_analysis,
    inputs=[gr.Dropdown(choices=["Arabic","English"],
            label="Select a Language",
            value="Arabic"),
            gr.File(label="Upload a file")],
    outputs=[gr.DataFrame(label="Results"),
             gr.Plot(label="Bar plot")],
    title="Sentiment-Analysis",
    description="Gradio interface that allows users to Choose what language the sentences will be and upload a text file containing the sentences to be analyzed, the sentences will be classified and results will be in a formatted table along with a plot sentiment distribution"
)

demo.launch(debug=True)