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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
"""
"""#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() |