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# Import the required Libraries | |
import gradio as gr | |
import numpy as np | |
import pandas as pd | |
import pickle | |
import transformers | |
from transformers import AutoTokenizer, AutoConfig,AutoModelForSequenceClassification,TFAutoModelForSequenceClassification, pipeline | |
from scipy.special import softmax | |
# Requirements | |
model_path = "KwabenaMufasa/Finetuned-Distilbert-base-model" | |
tokenizer = AutoTokenizer.from_pretrained(model_path) | |
config = AutoConfig.from_pretrained(model_path) | |
model = AutoModelForSequenceClassification.from_pretrained(model_path) | |
#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) | |
#Process the input and return prediction | |
def sentiment_analysis(text): | |
text = preprocess(text) | |
encoded_input = tokenizer(text, return_tensors = "pt") # for PyTorch-based models | |
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 | |
#Gradio app interface | |
app = gr.Interface(fn = sentiment_analysis, | |
inputs = gr.Textbox("Write your text or tweet here"), | |
outputs = "label", | |
title = "Twitter Sentiment Analyzer App", | |
description = "Vaccinate or Do Not Vaccinate", | |
interpretation = "default", | |
examples = [["Being vaccinated is actually awesome :)"]] | |
) | |
app.launch() |