app.py

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

# Load the model and tokenizer
model_name = "ahmetyaylalioglu/text-emotion-classifier"  # Replace with your actual model path
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Function to predict emotion
def predict_emotion(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
    with torch.no_grad():
        outputs = model(**inputs)

    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
    prediction = torch.argmax(probabilities, dim=-1).item()

    emotion = model.config.id2label[prediction]
    confidence = probabilities[0][prediction].item()

    return f"Emotion: {emotion}\nConfidence: {confidence:.2f}"

# Create Gradio interface
iface = gr.Interface(
    fn=predict_emotion,
    inputs=gr.Textbox(lines=2, placeholder="Enter text here..."),
    outputs="text",
    title="Emotion Classifier",
    description="Enter some text and click 'Submit' to predict the emotion."
)

# Launch the app
iface.launch()