Upload app.py

app.py

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+import streamlit as st
+import torch
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Set up Streamlit
+st.title("Emotion Detection with Transformers")
+
+# Text input
+user_input = st.text_area("Enter your text:")
+
+
+# Function to load model and tokenizer using @st.cache_data
+@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
+
+
+tokenizer, model = load_model_and_tokenizer()
+
+
+# Function to analyze emotion
+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()}"
+
+
+# Analyze button
+if st.button("Analyze Emotion"):
+    result = analyze_emotion(user_input)
+    st.write(result)