app.py

import gradio as gr
from transformers import pipeline
import re

# Load your SST model pipeline
sst_classifier = pipeline(
    "text-classification",
    model="SamanthaStorm/tether-sst",  # replace with your model name
    top_k=None,
    truncation=True
)

# Define lexicons
negations = {"not", "never", "no", "none", "hardly", "rarely", "barely", "doesn’t", "isn’t", "wasn’t"}
amplifiers = {"very", "extremely", "really", "absolutely", "so", "incredibly", "deeply", "profoundly"}
diminishers = {"somewhat", "slightly", "barely", "kind of", "sort of", "a little"}

def apply_lexicon_rules(text):
    tokens = text.lower().split()
    lexicon_score = 0

    for i, token in enumerate(tokens):
        if token in negations:
            lexicon_score -= 1
        if token in amplifiers:
            lexicon_score += 1
        if token in diminishers:
            lexicon_score -= 0.5

    return lexicon_score

def classify_sentiment(text):
    lex_score = apply_lexicon_rules(text)
    result = sst_classifier(text)[0]
    label = result["label"]
    base_score = result["score"]

    # Adjust model score based on lexicon features
    adjusted_score = min(max(base_score + (lex_score * 0.05), 0), 1)
    score_pct = round(adjusted_score * 100, 2)

    if label == "LABEL_0":
        return f"Supportive ({score_pct}%)"
    elif label == "LABEL_1":
        return f"Undermining ({score_pct}%)"
    else:
        return f"Unknown label ({label})"

iface = gr.Interface(
    fn=classify_sentiment,
    inputs=gr.Textbox(lines=4, placeholder="Paste message here..."),
    outputs="text",
    title="Tether SST (Lexicon-Enhanced)",
    description="Combines transformer model with lexicon-based adjustment to classify Supportive or Undermining tone."
)

iface.launch()