app.py

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

# Load your fine-tuned model from the Hugging Face Hub
model_name = "Wisaba/emotion_roberta_weighted"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Move to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Labels
emotion_labels = ["sadness", "joy", "love", "anger", "fear", "surprise"]

def classify_emotion(text):
    # 1. Tokenize
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
    inputs = {k: v.to(device) for k, v in inputs.items()}

    # 2. Predict
    with torch.no_grad():
        outputs = model(**inputs)

    # 3. Get Label
    logits = outputs.logits
    predicted_class_id = torch.argmax(logits, dim=-1).item()
    return emotion_labels[predicted_class_id]

# Define the Gradio Interface
iface = gr.Interface(
    fn=classify_emotion,
    inputs=gr.Textbox(lines=2, placeholder="Type how you feel...", label="Text Input"),
    outputs=gr.Textbox(label="Predicted Emotion"),
    title="Emotion Analysis (RoBERTa)",
    description="This model classifies text into 6 emotions: Sadness, Joy, Love, Anger, Fear, Surprise.",
    examples=[
        ["I am feeling so lonely and sad today."],
        ["I'm incredibly excited about the new project!"],
        ["Why did you do that? I'm so mad at you!"]
    ]
)

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