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import streamlit as st
from transformers import pipeline
@st.cache_resource
def load_classifier():
return pipeline(
"zero-shot-classification",
model="joeddav/xlm-roberta-large-xnli", # or use a smaller model
cache_dir="./hf_cache"
)
classifier = load_classifier()
CATEGORIES = {
"Family": "కుటుంబం",
"Friendship": "స్నేహం",
"Morality": "నీతి",
"Hard Work": "శ్రమ",
"Knowledge": "జ్ఞానం",
"Devotion": "భక్తి",
"Culture": "సంస్కృతి",
"Literature": "సాహిత్యం",
"Humility": "వినయం",
"Patience": "సహనం",
"Courage": "ధైర్యం",
"Arrogance": "అహంకారం",
"Love": "ప్రేమ",
"Greed": "దురాశ",
"Wisdom": "ఆలోచన",
"Responsibility": "బాధ్యత",
"Satire": "వ్యంగ్యం",
"Politics": "రాజకీయం",
"Wealth": "ధనము",
"Time": "సమయం"
}
def classify_proverb(text):
result = classifier(text, list(CATEGORIES.keys()))
top_label = result["labels"][0]
return CATEGORIES[top_label]
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