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import gradio as gr
from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer, AutoConfig
import numpy as np
from scipy.special import softmax
# Setup
model_path = f"GhylB/Sentiment_Analysis_DistilBERT"
tokenizer = AutoTokenizer.from_pretrained(model_path)
config = AutoConfig.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
# Functions
# Preprocess text (username and link placeholders)
def preprocess(text):
new_text = []
for t in text.split(" "):
t = '@user' if t.startswith('@') and len(t) > 1 else t
t = 'http' if t.startswith('http') else t
new_text.append(t)
return " ".join(new_text)
def sentiment_analysis(text):
text = preprocess(text)
# PyTorch-based models
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores_ = output[0][0].detach().numpy()
scores_ = softmax(scores_)
# Format output dict of scores
labels = ['Negative', 'Neutral', 'Positive']
scores = {l: float(s) for (l, s) in zip(labels, scores_)}
return scores
demo = gr.Interface(
fn=sentiment_analysis,
inputs=gr.Textbox(placeholder="Copy and paste/Write a tweet here..."),
outputs="text",
interpretation="default",
examples=[["What's up with the vaccine"],
["Covid cases are increasing fast!"],
["Covid has been invented by Mavis"],
["I'm going to party this weekend"],
["Covid is hoax"]],
title="Tutorial : Sentiment Analysis App",
description="This Application assesses if a twitter post relating to vaccinations is positive, neutral, or negative.", )
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860) # 8080
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