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| 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) |