Create app.py

app.py

@@ -0,0 +1,36 @@
+import streamlit as st
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import torch
+
+def generate_summary(model, tokenizer, dialogue):
+    # Tokenize input dialogue
+    inputs = tokenizer(dialogue, return_tensors="pt", max_length=1024, truncation=True)
+
+    # Generate summary
+    with torch.no_grad():
+        summary_ids = model.generate(inputs["input_ids"], max_length=150, length_penalty=0.8, num_beams=4)
+
+    # Decode and return the summary
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
+    return summary
+
+
+st.title("Dialog Summarizer App")
+
+# User input
+user_input = st.text_area("Enter the dialog:")
+if not user_input:
+    st.info("Please enter a dialog.")
+    return
+
+# Load pre-trained Pegasus model and tokenizer
+model_name = "ale-dp/pegasus-finetuned-dialog-summarizer"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# Generate summary
+summary = generate_summary(model, tokenizer, user_input)
+
+# Display the generated summary
+st.subheader("Summary:")
+st.write(summary)