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import os
os.system("git lfs pull")
import streamlit as st
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import numpy as np
st.set_page_config(page_title="Emotion Classifier", layout="centered")
# Load GoEmotions label names
GOEMOTIONS_LABELS = [
"admiration", "amusement", "anger", "annoyance", "approval", "caring",
"confusion", "curiosity", "desire", "disappointment", "disapproval",
"disgust", "embarrassment", "excitement", "fear", "gratitude", "grief",
"joy", "love", "nervousness", "optimism", "pride", "realization", "relief",
"remorse", "sadness", "surprise", "neutral"
]
# -----------------------------
# Load model and tokenizer
# -----------------------------
MODEL_PATH = "/mount/src/emotion-classifier-nlp/outputs/model"
model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH, device_map="auto", low_cpu_mem_usage=True)
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
print("Model device:", next(model.parameters()).device)
# -----------------------------
# Streamlit UI
# -----------------------------
st.title("🧠 Emotion Classifier (NLP)")
st.markdown("Enter a sentence to analyze:")
input_text = st.text_area(" ", height=100)
if st.button("Classify") and input_text.strip():
with torch.no_grad():
inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
outputs = model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=1)[0]
pred_label_idx = torch.argmax(probs).cpu().item()
pred_score = probs[pred_label_idx].cpu().item()
pred_emotion = GOEMOTIONS_LABELS[pred_label_idx]
# -----------------------------
# Confidence threshold logic
# -----------------------------
threshold = 0.6
if pred_score < threshold:
st.warning(f"**Predicted Emotion:** Unclear / Not enough signal (Confidence: {pred_score:.0%})")
else:
st.success(f"**Predicted Emotion:** {pred_emotion} (Confidence: {pred_score:.0%})")
# -----------------------------
# Show all probabilities
# -----------------------------
st.markdown("### Prediction Probabilities:")
for i, prob in enumerate(probs):
st.write(f"- {GOEMOTIONS_LABELS[i]}: {prob.item():.4f}")
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