app.py

from fastapi import FastAPI
import gradio as gr

from transformers import pipeline
from gradio.components import Textbox

app = FastAPI()

# Load the sentiment analysis pipeline with DistilBERT
distilbert_pipeline = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
label_map = {"POSITIVE":"OTHER", "NEGATIVE":"SENSITIVE"}

input1 = Textbox(lines=2, placeholder="Type your text here...")

@app.get("/")
async def root():
    def predict_sentiment(text):
        """
        Predicts the sentiment of the input text using DistilBERT.
        :param text: str, input text to analyze.
        :return: str, predicted sentiment and confidence score.
        """
        result = distilbert_pipeline(text)[0]
        label = label_map[result['label']]
        score = result['score']
        return f"TAG: {label}, Confidence: {score:.2f}"

    # Create a Gradio interface
    text_input = gr.Interface(fn=predict_sentiment,
                         inputs=input1,
                         outputs="text",
                         title="Talk2Loop Sensitive statement tags",
                         description="This model predicts the sensitivity of the input text. Enter a sentence to see if it's sensitive or not.")

    return text_input.launch(share=True, host="0.0.0.0", port=8000)

# Launch the interface
app = gr.mount_gradio_app(app, text_input, path="/")
# iface.launch(port=8000)