app.py

import gradio as gr
from transformers import pipeline

# 1) Load the HF pipeline with all scores so we can show probabilities
classifier = pipeline(
    "text-classification",
    model="j-hartmann/emotion-english-roberta-large",
    return_all_scores=True
)

# 2) Wrap it in a function that returns a label→score dict
def classify_emotion(text: str):
    scores = classifier(text)[0]   # returns list of {label, score}
    return {item["label"]: float(item["score"]) for item in scores}

# 3) Build the Gradio interface
iface = gr.Interface(
    fn=classify_emotion,
    inputs=gr.Textbox(
        lines=2,
        placeholder="Type any English sentence here…",
        label="Input Text"
    ),
    outputs=gr.Label(
        num_top_classes=6,
        label="Emotion Probabilities"
    ),
    examples=[
        ["I love you!"],
        ["The movie was heart breaking!"]
    ],
    title="English Emotion Classifier",
    description=(
        "Predicts one of Ekman's 6 basic emotions plus neutral "
        "(anger 🤬, disgust 🤢, fear 😨, joy 😀, neutral 😐, "
        "sadness 😭, surprise 😲)."
    )
)

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