app.py

from transformers import RobertaTokenizer, AutoModelForSequenceClassification
from scipy.special import expit
import numpy as np
import os
import gradio as gr
import requests
from datetime import datetime

import transformers.utils.hub as _hub
_hub.list_repo_templates = lambda *args, **kwargs: []  # no-op

# set up model
authtoken = os.environ.get("TOKEN")

tokenizer = RobertaTokenizer.from_pretrained(
    "guidecare/feelings_and_issues_large_v2",
    use_safetensors=True,
    use_auth_token=authtoken
)
tokenizer.do_lower_case = True
model = AutoModelForSequenceClassification.from_pretrained(
    "guidecare/feelings_and_issues_large_v2",
    use_safetensors=True,
    use_auth_token=authtoken
)

all_label_names = list(model.config.id2label.values())

def predict(text):
    probs = expit(model(**tokenizer([text], return_tensors="pt", padding=True)).logits.detach().numpy())
    probs = [float(np.round(i, 2)) for i in probs[0]]
    zipped_list = list(zip(all_label_names, probs))
    print(text, zipped_list)
    
    issues = [(i, j) for i, j in zipped_list if i.startswith('issue')]
    feelings = [(i, j) for i, j in zipped_list if i.startswith('feeling')]
    harm = [(i, j) for i, j in zipped_list if i.startswith('harm')]
    sentiment = [(i, j) for i, j in zipped_list if i.startswith('sentiment')]
    
    issues = sorted(issues, key=lambda x: x[1], reverse=True)
    feelings = sorted(feelings, key=lambda x: x[1], reverse=True)
    harm = sorted(harm, key=lambda x: x[1], reverse=True)
    sentiment = sorted(sentiment, key=lambda x: x[1], reverse=True)
    
    top = issues + feelings + harm + sentiment
    d = {i: j for i, j in top}
    return d

iface = gr.Interface(
    fn=predict,
    inputs=gr.Textbox(label="Enter text"),
    outputs=gr.Label(label="Predictions"),
    title="Emotion and Issue Predictor",
    description="Enter a text to predict emotions and issues.",
)

if __name__ == "__main__":
    iface.launch()