app.py

import gradio as gr
import torch
from transformers import AutoModelForSequenceClassification
from transformers import TFAutoModelForSequenceClassification 
from transformers import AutoTokenizer, AutoConfig
from scipy.special import softmax

#setup
model_path = "KAITANY/finetuned-roberta-base-sentiment"

tokenizer = AutoTokenizer.from_pretrained(model_path)
#config = AutoConfig.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)

def preprocess(text):
    # Preprocess text (username and link placeholders)
    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)

    # Tokenize the text
    inputs = tokenizer(text, return_tensors="pt", padding=True)

    # Make a prediction
    with torch.no_grad():
        outputs = model(**inputs)

    # Get the predicted class probabilities
    scores = torch.softmax(outputs.logits, dim=1).tolist()[0]

    # Map the scores to labels
    labels = ['Negative', 'Neutral', 'Positive']
    scores_dict = {label: score for label, score in zip(labels, scores)}

    return scores_dict

title = "Sentiment Analysis Application\n\n\nThis application assesses if a twitter post relating to vaccination is positive,neutral or negative"


demo = gr.Interface(
    fn=sentiment_analysis,
    inputs=gr.Textbox(placeholder="Write your tweet here..."),
    outputs=gr.Label(num_top_classes=3),
    examples=[["The Vaccine is harmful!"],["I cant believe people don't vaccinate their kids"],["FDA think just not worth the AE unfortunately"],["For a vaccine given to healthy"]],
     title=title
)

demo.launch(share=True)