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ConvoBrief.py

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+import transformers
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM,pipeline
+from peft import PeftModel, PeftConfig
+import streamlit as st
+
+@st.cache_resource
+def load_model():
+    config = PeftConfig.from_pretrained("Ketan3101/ConvoBrief")
+    model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn")
+    model = PeftModel.from_pretrained(model, "Ketan3101/ConvoBrief")
+    tokenizer=AutoTokenizer.from_pretrained("facebook/bart-large-cnn")
+    return model, tokenizer
+
+def main():
+    st.set_page_config(page_title="ConvoBrief", page_icon="📝")
+    model,tokenizer=load_model()
+    st.title("ConvoBrief: A dialogue summarizer")
+    dialogue=st.text_area("Enter the Dialogue")
+
+    if st.button("Summarize Dialogue"):
+        if dialogue:
+            inputs=tokenizer(dialogue,return_tensors='pt')
+            summary=tokenizer.decode(
+                model.generate(input_ids=inputs['input_ids'], max_new_tokens=200, temperature=1.2001, do_sample=True)[0],
+                skip_special_tokens=True
+            )
+
+            st.subheader("Summarized Dialogue:")
+            st.write(summary)
+            st.error("The model has been trained on less parameters, so their might be minor errors")
+
+        else:
+            st.warning("No! Dialogue was given")
+
+if __name__=="__main__":
+    main()