app.py

import gradio as gr
import torch
from transformers import AutoTokenizer
from modeling import BERTMultiLabel

LABELS = ["anger", "fear", "joy", "sadness", "surprise"]

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("./")
model = BERTMultiLabel("microsoft/deberta-v3-base", num_labels=len(LABELS))

state = torch.load("pytorch_model.bin", map_location="cpu")
model.load_state_dict(state)
model.eval()


# ---------------- PREDICTION FUNCTION ---------------- #
def predict(text):
    if not text.strip():
        return {"error": "Please enter text."}

    enc = tokenizer(
        text,
        truncation=True,
        padding="max_length",
        max_length=128,
        return_tensors="pt"
    )

    with torch.no_grad():
        logits = model(enc["input_ids"], enc["attention_mask"])
        probs = torch.sigmoid(logits)[0].tolist()

    scores = {label: round(p, 4) for label, p in zip(LABELS, probs)}
    mood = LABELS[int(torch.tensor(probs).argmax())]

    emoji_map = {
        "anger": "😡",
        "fear": "😨",
        "joy": "😊",
        "sadness": "😢",
        "surprise": "😮",
    }

    return {
        "Predicted Mood": f"{emoji_map[mood]} {mood.capitalize()}",
        "Scores": scores,
    }


# ---------------- UI LAYOUT ---------------- #
with gr.Blocks(title="Mood Detection of the User - DeBERTa") as demo:

    gr.Markdown("""
        <div style="text-align:center;">
            <h1 style="font-size:3rem;">🎭 Emotion Detection with DeBERTa-v3</h1>
            <p style="font-size:1.1rem; color:#555;">
                Multi-label emotion classification powered by DeBERTa-v3 <br>
                Trained on IIT Madras Deep Learning & GenAI Dataset (2025)
            </p>
        </div>
        <br>
    """)

    with gr.Row():

        with gr.Column(scale=1):

            gr.HTML("""
                <div style="
                    background:white; padding:20px; border-radius:14px;
                    box-shadow:0 2px 12px rgba(0,0,0,0.08); margin-bottom:20px;
                ">
                    <h2>📌 Model Overview</h2>
                    <ul style="line-height:1.6;">
                        <li><b>Architecture:</b> DeBERTa-v3 Base</li>
                        <li><b>Task:</b> Multi-label Emotion Detection</li>
                        <li><b>Labels:</b> Anger, Fear, Joy, Sadness, Surprise</li>
                        <li><b>Training:</b> AdamW + BCEWithLogitsLoss</li>
                        <li><b>Sequence Length:</b> 128 tokens</li>
                        <li><b>Framework:</b> PyTorch + Transformers</li>
                    </ul>
                </div>
            """)

            gr.HTML("""
                <div style="
                    background:white; padding:20px; border-radius:14px;
                    box-shadow:0 2px 12px rgba(0,0,0,0.08); margin-bottom:20px;
                ">
                    <h2>📚 Dataset Details</h2>
                    <p>Dataset: IIT Madras DL-GenAI Multi-Label Emotion Dataset</p>
                    <ul>
                        <li>😠 Anger</li>
                        <li>😨 Fear</li>
                        <li>😊 Joy</li>
                        <li>😢 Sadness</li>
                        <li>😲 Surprise</li>
                    </ul>
                    <p><b>Metric:</b> Macro F1 Score</p>
                </div>
            """)

            gr.HTML("""
                <div style="
                    background:white; padding:20px; border-radius:14px;
                    box-shadow:0 2px 12px rgba(0,0,0,0.08);
                ">
                    <h2>🏆 Competition Summary</h2>
                    <ul style="line-height:1.6;">
                        <li><b>Platform:</b> Kaggle Private Competition</li>
                        <li><b>Course:</b> IIT Madras - Deep Learning & GenAI</li>
                        <li><b>Final Rank:</b> 27 / 200 Participants</li>
                        <li><b>Public LB:</b> 87.8% Macro F1</li>
                        <li><b>Private LB:</b> 87.0% Macro F1</li>
                        <li><b>Models Attempted:</b> CNN | GRU | BiLSTM | DistilBERT | DeBERTa</li>
                    </ul>
                </div>
            """)

        with gr.Column(scale=2):

            input_box = gr.Textbox(
                label="Enter your text",
                placeholder="Example: I feel amazing today! 🎉",
                lines=4,
            )

            btn = gr.Button("🎯 Analyze Emotion", elem_id="analyze-button")

            output = gr.JSON(label="Model Output")

            btn.click(predict, inputs=input_box, outputs=output)

    gr.Markdown("""
        <br>
        <p style="text-align:center; color:#777;">
            Built by <b>Ayusman Samasi</b> • IIT Madras Deep Learning & GenAI
        </p>
    """)

demo.launch()