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import os | |
import gradio as gr | |
from transformers import AutoTokenizer, AutoModel | |
from scipy.special import softmax | |
from huggingface_hub import login | |
# Load environment variables | |
from dotenv import load_dotenv | |
load_dotenv() | |
login(os.getenv("access_token")) | |
# Requirements | |
model_path = "imalexianne/distilbert-base-uncased" | |
# tokenizer = AutoTokenizer.from_pretrained(model_path) | |
tokenizer = AutoTokenizer.from_pretrained(model_path) | |
model = AutoModel.from_pretrained(model_path) | |
# Preprocessing function | |
def preprocess(text): | |
new_text = [] | |
for x in text.split(" "): | |
x = "@user" if x.startswith("@") and len(x) > 1 else x | |
x = "http" if x.startswith("http") else x | |
new_text.append(x) | |
return " ".join(new_text) | |
# Function to 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 here..."), | |
outputs="label", | |
title="Sentiment Analysis of Tweets on COVID-19 Vaccines", | |
description="Sentiment Analysis of text based on tweets about COVID-19 Vaccines using a fine-tuned 'distilbert-base-uncased' model", | |
examples=[["Covid vaccination has no positive impact"]] | |
) | |
app.launch() | |