Streamlit app

app.py

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+import streamlit as st
+import torch
+from transformers import(
+    T5TokenizerFast as T5Tokenizer)
+import warnings
+warnings.filterwarnings("ignore")
+
+MODEL_NAME = "t5-small"
+tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME)
+device = torch.device('cpu')
+model = torch.load('models.pth', map_location=device)
+
+
+def summarize(text):
+    text_encoding = tokenizer(
+        text,
+        max_length=512,
+        padding="max_length",
+        truncation=True,
+        return_attention_mask=True,
+        add_special_tokens=True,
+        return_tensors="pt")
+
+    generated_ids = model.generate(
+        input_ids=text_encoding["input_ids"],
+        attention_mask=text_encoding["attention_mask"],
+        max_length=150,
+        num_beams=2,
+        repetition_penalty=2.5,
+        length_penalty=1.0,
+        early_stopping=True)
+
+    preds = [
+        tokenizer.decode(gen_id, skip_special_tokens = True, clean_up_tokenization_spaces=True)
+        for gen_id in generated_ids
+    ]
+
+    return "".join(preds)
+
+
+def main():
+    """Text Summarizer app with streamlit"""
+    st.title("T5 text summarizer with streamlit")
+    st.subheader("Summarize your 512 words here!")
+    message = st.text_area("Enter your text", "Type Here")
+    if st.button("Summarize text"):
+        summary_results = summarize(message)
+
+
+if __name__ == '__main__':
+    main()