init

app.py

@@ -0,0 +1,29 @@
+import streamlit as st
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+
+# Load the model and tokenizer
+model_name = 'utrobinmv/t5_summary_en_ru_zh_base_2048'
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+
+def summarize_text(text, prefix):
+    src_text = prefix + text
+    input_ids = tokenizer(src_text, return_tensors="pt")
+    generated_tokens = model.generate(**input_ids)
+    result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+    return result[0]
+
+st.title('Text Summarization App')
+
+input_text = st.text_area("Enter the text to summarize:", height=300)
+
+if st.button("Generate Summaries"):
+    if input_text:
+        title1 = summarize_text(input_text, 'summary: ')
+        title2 = summarize_text(input_text, 'summary brief: ')
+        st.write("### Title 1")
+        st.write(title1)
+        st.write("### Title 2")
+        st.write(title2)
+    else:
+        st.warning("Please enter some text to summarize.")