Spaces:
Runtime error
Runtime error
File size: 1,697 Bytes
8b3f680 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
import gradio as gr
import numpy as np
import torch
from transformers import AutoModelForSequenceClassification
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer, AutoConfig
from scipy.special import softmax
# Setup
model_path = f"pakornor/roberta-base"
tokenizer = AutoTokenizer.from_pretrained('roberta-base')
config = AutoConfig.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
# Functions
# Preprocess text
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)
# Input preprocessing
def sentiment_analysis(text):
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores_ = output[0][0].detach().numpy()
scores_ = softmax(scores_)
# Format output dictionary of scores
labels = ['Negative', 'Neutral', 'Positive']
scores = {l:float(s) for (l,s) in zip(labels, scores_) }
return scores
# Gradio App
app = gr.Interface(
fn=sentiment_analysis,
inputs=gr.Textbox("Input tweet here:"),
outputs="label",
title="Sentiment Analysis of Tweets on Covid-19 Vaccines",
description="With this App, you can type Tweets related to the Covid Vaccine and the app will rate the sentiment of the tweet..!",
examples=[["Be careful of covid vaccination"],
["The vaccine can reduce your immunity to diseases"],
["I cant wait for the Covid Vaccine!"]]
)
app.launch() |