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