Create app.py

app.py

@@ -0,0 +1,25 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load the tokenizer and model
+@st.cache_resource
+def load_model():
+    model_name = "your-huggingface-username/your-model-repo"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+st.title("Summarization with BART")
+
+# Text input
+dialogue = st.text_area("Enter the dialogue to summarize:", height=200)
+
+# Summarize button
+if st.button("Summarize"):
+    inputs = tokenizer(dialogue, max_length=512, truncation=True, return_tensors="pt")
+    summary_ids = model.generate(inputs["input_ids"], max_length=128, min_length=30, length_penalty=2.0, num_beams=4, early_stopping=True)
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    st.write("### Summary")
+    st.write(summary)