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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() |