added summarizercode

app.py

@@ -1,4 +1,11 @@
 import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+  
+#downloading tokenizer and model 
+tokenizer = AutoTokenizer.from_pretrained("tuner007/pegasus_summarizer")
+model = AutoModelForSeq2SeqLM.from_pretrained("tuner007/pegasus_summarizer")
+
+
 
 st.markdown(""" <style> .font {
 font-size:50px ; font-family: "Helvetica"; color: #FF9633;} 
@@ -24,16 +31,27 @@ st.markdown(""" #### LorSor helps you through a simple 3 stage process.
 
     Send any feedback to [us](mailto:[email protected]) """)
 
-
+st.markdown('#')
 col1, col2 = st.columns(2)
 
 with col1:
     col1.header("Step 1:")
-    x = st.text_area('Paste the full article text to summarize here...')
+    raw_text = st.text_area('Paste the full article text to summarize here...')
     st.button("Summarize this")
 
+def get_response(input_text):
+    batch = tokenizer([input_text],truncation=True,padding='longest',max_length=1024, return_tensors="pt").to('cpu')
+    gen_out = model.generate(**batch,max_length=128,num_beams=5, num_return_sequences=1, temperature=1.5)
+    output_text = tokenizer.batch_decode(gen_out, skip_special_tokens=True)
+    return output_text
+
+if len(raw_text) < 10:
+    summary = "Lorem Ipsum is a long and boring piece of old latin text. What it means i have no idea"
+else:
+    summary = get_response(raw_text)
+
+
 with col2:
     col2.header("Step 2:")
-    summary = "Lorem Ipsum is a long and boring piece of old latin text. What it means i have no idea"
     y = st.text_area("Here is the completed summary for you to edit", summary)
     st.button("Submit edits")