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Upload sentiment_analysis_m2_s3_(3).py
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sentiment_analysis_m2_s3_(3).py
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# -*- coding: utf-8 -*-
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"""sentiment_analysis_M2_S3 (3).ipynb
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Automatically generated by Colab.
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Original file is located at
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https://colab.research.google.com/drive/122LsK0EllcEargr6R8LmbYYtcnCb5PV6
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Installations
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"""
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!pip install gradio --quiet
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!pip install transformers --quiet
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"""#Let's build a demo for a sentiment analysis task !
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---
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Import the necessary modules :
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"""
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import numpy as np
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import gradio as gr
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from transformers import pipeline
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"""Import the pipeline :"""
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sentiment =pipeline("sentiment-analysis", verbose = 0)
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"""Test the pipeline on these reviews (you can also test on your own reviews) :"""
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reviews= ["I really enjoyed my stay !", "Worst rental I ever got"]
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"""What is the format of the output ? How can you get only the sentiment or the confidence score ?"""
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sentiment(reviews)
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"""Create a function that takes a text in input, and returns a sentiment, and a confidence score as 2 different variables"""
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def sentiment(prompt):
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# This is where you would integrate with an actual sentiment analysis model
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# For this example, we'll use simple rules to simulate the behavior
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if "good" in prompt.lower():
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return [{'label': 'Positive', 'score': 0.9}]
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elif "bad" in prompt.lower():
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return [{'label': 'Negative', 'score': 0.9}]
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else:
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return [{'label': 'Neutral', 'score': 0.5}]
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def get_sentiment(prompt):
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result = sentiment(prompt)
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return result[0]['label'], result[0]['score']
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"""Build an interface for the app using Gradio.
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The customer wants this result :
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"""
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textbox = gr.Textbox(label="Enter the review:")
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textbox_sen = gr.Textbox(label="Sentiment")
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textbox_score = gr.Textbox(label="Score")
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interface = gr.Interface(
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fn=get_sentiment,
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inputs=textbox,
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outputs=[textbox_sen, textbox_score],
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title="Sentiment Analysis Prototype"
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)
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interface.launch()
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"""## Arabic sentiment analysis"""
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sa = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment')
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def sentiment(prompt):
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result = sa(prompt)
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return result
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def get_sentiment(prompt):
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result = sentiment(prompt)
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label = result[0]['label']
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score = result[0]['score']
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return label, score
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textbox = gr.Textbox(label="قم بادخال الرأي:")
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textbox_sen = gr.Textbox(label="الشعور")
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textbox_score = gr.Textbox(label="النسبة")
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interface = gr.Interface(
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fn=get_sentiment,
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inputs=textbox,
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outputs=[textbox_sen, textbox_score],
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title="النموذج الأولي لتحليل المشاعر"
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)
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interface.launch()
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"""## classify sentiments expressed through text or emojis:
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"""
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import gradio as gr
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from transformers import pipeline
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# Initialize the sentiment analysis pipeline with a potentially better model
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model_name = "cardiffnlp/twitter-roberta-base-sentiment"
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sa = pipeline('sentiment-analysis', model=model_name)
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def classify_emoji(prompt):
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result = sa(prompt)
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label = result[0]['label']
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score = result[0]['score']
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# Map the label to a user-friendly sentiment
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if label == 'LABEL_2': # Assuming LABEL_2 is positive
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sentiment = "Positive"
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elif label == 'LABEL_0': # Assuming LABEL_0 is negative
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sentiment = "Negative"
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elif label == 'LABEL_1': # Assuming LABEL_1 is neutral
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sentiment = "Neutral"
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return sentiment, f"{score:.2f}"
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textbox = gr.Textbox(label="Enter the emoji or text:")
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textbox_sen = gr.Textbox(label="Sentiment")
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textbox_score = gr.Textbox(label="Confidence Score")
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interface = gr.Interface(
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fn=classify_emoji,
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inputs=textbox,
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outputs=[textbox_sen, textbox_score],
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title="Emoji Sentiment Classification",
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description="Enter an emoji or text to classify its sentiment. The model will return the sentiment and a confidence score.",
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theme="compact"
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)
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interface.launch()
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