app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import numpy as np

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("michellejieli/emotion_text_classifier")
model = AutoModelForSequenceClassification.from_pretrained("michellejieli/emotion_text_classifier")

# Function to classify emotions
def classify_emotion(text):
    inputs = tokenizer(text, return_tensors="pt")
    outputs = model(**inputs)
    logits = outputs.logits
    probabilities = torch.nn.functional.softmax(logits, dim=-1)
    probs = probabilities.detach().numpy()[0]
    labels = ["anger", "disgust", "fear", "joy", "neutral", "sadness", "surprise"]
    results = {label: prob for label, prob in zip(labels, probs)}
    return results

# Gradio interface setup
iface = gr.Interface(
    fn=classify_emotion,
    inputs=gr.Textbox(lines=2, placeholder="Enter a sentence to analyze emotions", label="Input Text"),
    outputs=gr.Label(label="Emotion Probabilities"),
    title="Emotion Classifier",
    description="Enter a sentence and see the probabilities of different emotions."
)

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