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import streamlit as st |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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st.title("Emotion Detection with Transformers") |
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user_input = st.text_area("Enter your text:") |
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@st.cache_data() |
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def load_model_and_tokenizer(): |
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model_name = "mrm8488/t5-base-finetuned-emotion" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
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return tokenizer, model |
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tokenizer, model = load_model_and_tokenizer() |
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def analyze_emotion(text): |
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if text.strip() == "": |
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return "Please enter some text to analyze." |
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input_ids = tokenizer.encode(text + '</s>', return_tensors='pt') |
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output = model.generate(input_ids=input_ids, |
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max_length=2) |
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dec = [tokenizer.decode(ids, skip_special_tokens=True) for ids in output] |
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label = dec[0] |
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return f"Emotion: {label.capitalize()}" |
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if st.button("Analyze Emotion"): |
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result = analyze_emotion(user_input) |
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st.write(result) |
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