app.py

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()

# Get the token from the environment variable
access_token = os.getenv("access_token")

# Log in to Hugging Face (commented out for now)
# login(access_token)

# Requirements
model_path = "imalexianne/distilbert-base-uncased"
# tokenizer = AutoTokenizer.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path, revision="main", use_auth_token=os.getenv("access_token"))

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")
    output = model(**encoded_input)
    scores_ = output.logits[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()