app.py

import os
import gradio
from PIL import Image
from timeit import default_timer as timer
from tensorflow import keras
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
import numpy as np

loaded_model = AutoModelForSequenceClassification.from_pretrained("runaksh/financial_sentiment_distilBERT")
loaded_tokenizer = AutoTokenizer.from_pretrained("runaksh/financial_sentiment_distilBERT")

# Function for class prediction
def predict(sample, validate=True):
  classifier = pipeline("text-classification", model=loaded_model, tokenizer=loaded_tokenizer)
  pred = classifier(sample)[0]['label']
  return pred

title = "Financial Sentiment Classification"
description = "Enter the news"

# Gradio elements

# Input from user
in_prompt = gradio.components.Textbox(lines=2, label='Enter the News')

# Output response
out_response = gradio.components.Textbox(label='Sentiment')

# Gradio interface to generate UI link 
iface = gradio.Interface(fn=predict,
                         inputs = in_prompt,
                         outputs = out_response,
                         title=title,
                         description=description
                         )

iface.launch(debug = True)