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| # -*- coding: utf-8 -*- | |
| """sentiment_analysis_M2_S3 (3).ipynb | |
| Automatically generated by Colab. | |
| Original file is located at | |
| https://colab.research.google.com/drive/122LsK0EllcEargr6R8LmbYYtcnCb5PV6 | |
| Installations | |
| """ | |
| !pip install gradio --quiet | |
| !pip install transformers --quiet | |
| """#Let's build a demo for a sentiment analysis task ! | |
| --- | |
| Import the necessary modules : | |
| """ | |
| import numpy as np | |
| import gradio as gr | |
| from transformers import pipeline | |
| """Import the pipeline :""" | |
| sentiment =pipeline("sentiment-analysis", verbose = 0) | |
| """Test the pipeline on these reviews (you can also test on your own reviews) :""" | |
| reviews= ["I really enjoyed my stay !", "Worst rental I ever got"] | |
| """What is the format of the output ? How can you get only the sentiment or the confidence score ?""" | |
| sentiment(reviews) | |
| """Create a function that takes a text in input, and returns a sentiment, and a confidence score as 2 different variables""" | |
| def sentiment(prompt): | |
| # This is where you would integrate with an actual sentiment analysis model | |
| # For this example, we'll use simple rules to simulate the behavior | |
| if "good" in prompt.lower(): | |
| return [{'label': 'Positive', 'score': 0.9}] | |
| elif "bad" in prompt.lower(): | |
| return [{'label': 'Negative', 'score': 0.9}] | |
| else: | |
| return [{'label': 'Neutral', 'score': 0.5}] | |
| def get_sentiment(prompt): | |
| result = sentiment(prompt) | |
| return result[0]['label'], result[0]['score'] | |
| """Build an interface for the app using Gradio. | |
| The customer wants this result : | |
|  | |
| """ | |
| textbox = gr.Textbox(label="Enter the review:") | |
| textbox_sen = gr.Textbox(label="Sentiment") | |
| textbox_score = gr.Textbox(label="Score") | |
| interface = gr.Interface( | |
| fn=get_sentiment, | |
| inputs=textbox, | |
| outputs=[textbox_sen, textbox_score], | |
| title="Sentiment Analysis Prototype" | |
| ) | |
| interface.launch() | |
| """## Arabic sentiment analysis""" | |
| sa = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment') | |
| def sentiment(prompt): | |
| result = sa(prompt) | |
| return result | |
| def get_sentiment(prompt): | |
| result = sentiment(prompt) | |
| label = result[0]['label'] | |
| score = result[0]['score'] | |
| return label, score | |
| textbox = gr.Textbox(label="قم بادخال الرأي:") | |
| textbox_sen = gr.Textbox(label="الشعور") | |
| textbox_score = gr.Textbox(label="النسبة") | |
| interface = gr.Interface( | |
| fn=get_sentiment, | |
| inputs=textbox, | |
| outputs=[textbox_sen, textbox_score], | |
| title="النموذج الأولي لتحليل المشاعر" | |
| ) | |
| interface.launch() | |
| """## classify sentiments expressed through text or emojis: | |
| """ | |
| import gradio as gr | |
| from transformers import pipeline | |
| # Initialize the sentiment analysis pipeline with a potentially better model | |
| model_name = "cardiffnlp/twitter-roberta-base-sentiment" | |
| sa = pipeline('sentiment-analysis', model=model_name) | |
| def classify_emoji(prompt): | |
| result = sa(prompt) | |
| label = result[0]['label'] | |
| score = result[0]['score'] | |
| # Map the label to a user-friendly sentiment | |
| if label == 'LABEL_2': # Assuming LABEL_2 is positive | |
| sentiment = "Positive" | |
| elif label == 'LABEL_0': # Assuming LABEL_0 is negative | |
| sentiment = "Negative" | |
| elif label == 'LABEL_1': # Assuming LABEL_1 is neutral | |
| sentiment = "Neutral" | |
| return sentiment, f"{score:.2f}" | |
| textbox = gr.Textbox(label="Enter the emoji or text:") | |
| textbox_sen = gr.Textbox(label="Sentiment") | |
| textbox_score = gr.Textbox(label="Confidence Score") | |
| interface = gr.Interface( | |
| fn=classify_emoji, | |
| inputs=textbox, | |
| outputs=[textbox_sen, textbox_score], | |
| title="Emoji Sentiment Classification", | |
| description="Enter an emoji or text to classify its sentiment. The model will return the sentiment and a confidence score.", | |
| theme="compact" | |
| ) | |
| interface.launch() |