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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import torch.nn.functional as F
MODEL_NAME = "imrgurmeet/fine-tuned-sentiment-model"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
st.title("Sentiment Analyzer(Encoder-Only)")
user_input = st.text_area("Enter text for sentiment analysis:")
if st.button("Analyze"):
if user_input.strip() != "":
inputs = tokenizer(user_input, return_tensors="pt")
outputs = model(**inputs)
probs = F.softmax(outputs.logits, dim=-1)
# Dynamically create label list based on model
num_labels = model.config.num_labels
if num_labels == 3:
labels = ["Negative", "Neutral", "Positive"]
elif num_labels == 2:
labels = ["Negative", "Positive"]
else:
labels = [f"Class {i}" for i in range(num_labels)]
pred_class = torch.argmax(probs).item()
sentiment = labels[pred_class]
st.write(f"**Sentiment:** {sentiment}")
st.write(f"**Confidence:** {probs[0][pred_class]:.2f}")
else:
st.warning("Please enter some text.")
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