import gradio as gr
from transformers import pipeline
import nltk

# Download nltk sentence tokenizer data once
nltk.download('punkt')
nltk.download('punkt_tab')
from nltk.tokenize import sent_tokenize

classifier = pipeline(
    "text-classification", 
    model="j-hartmann/emotion-english-distilroberta-base",
    top_k=1
)

def predict_emotion(text):
    sentences = sent_tokenize(text)  # Split text into sentences
    results = []
    for sentence in sentences:
        result = classifier(sentence)
        # Handle nested list result if any
        if isinstance(result[0], list):
            prediction = result[0][0]
        else:
            prediction = result[0]
        label = prediction["label"]
        score = prediction["score"]
        results.append(f"{sentence.strip()} - Emotion: {label}, Confidence: {score:.2f}")
    return "\n".join(results)

demo = gr.Interface(
    fn=predict_emotion,
    inputs=gr.Textbox(lines=5, placeholder="Enter multiple sentences here...", label="Input Text"),
    outputs=gr.Textbox(label="Emotion per Sentence"),
    title="Emotion Classifier by Sentence",
    description="Enter text with multiple sentences. Each sentence's emotion and confidence is shown separately."
)

demo.launch()