Updated

app.py

@@ -1,23 +1,10 @@
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import pipeline
 import torch
 
-# Load pre-trained model and tokenizer
-model_name = "username/my_spam_detector"  # replace 'username' with your Hugging Face account name
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
+pipe = pipeline('sentiment-analysis')
+text = st.text_area("Enter text: ")
+if text:
+    out = pipe(text)
+    st.json(out)
 
-st.title("Spam Detector")
-
-text = st.text_input("Enter a text")
-
-if st.button('Predict'):
-    # Tokenize the input text
-    inputs = tokenizer(text, return_tensors='pt')
-
-    # Get model's prediction
-    outputs = model(**inputs)
-    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
-
-    # Show prediction
-    st.write(f"The probability of the text being spam is {probs[0][1].item() * 100:.2f}%.")