Create app.py

app.py

@@ -0,0 +1,34 @@
+import streamlit as st
+import requests
+import torch
+from transformers import pipeline
+from transformers import BartTokenizer, BartForConditionalGeneration
+
+# Replace with your Hugging Face model repository path
+model_repo_path = 'Muh113/husnain'
+
+# Load the model and tokenizer
+model = BartForConditionalGeneration.from_pretrained(model_repo_path)
+tokenizer = BartTokenizer.from_pretrained(model_repo_path)
+
+# Initialize the summarization pipeline
+summarizer = pipeline('summarization', model=model,tokenizer=tokenizer)
+
+# Streamlit app layout
+st.title("Text Summarization App")
+
+# User input
+text_input = st.text_area("Enter text to summarize", height=300)
+
+# Summarize the text
+if st.button("Summarize"):
+    if text_input:
+        with st.spinner("Generating summary..."):
+            try:
+                summary = summarizer(text_input, max_length=150, min_length=30, do_sample=False)
+                st.subheader("Summary")
+                st.write(summary[0]['summary_text'])
+            except Exception as e:
+                st.error(f"Error during summarization: {e}")
+    else:
+        st.warning("Please enter some text to summarize.")