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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)
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