Create app.py

app.py

@@ -0,0 +1,51 @@
+import os
+os.system('pip install streamlit transformers torch')
+
+import streamlit as st
+from transformers import BartTokenizer, BartForConditionalGeneration
+
+# Load the model and tokenizer
+model_name = 'ahmadrocks/facebook_bart_base_new'
+
+tokenizer = BartTokenizer.from_pretrained(model_name)
+model = BartForConditionalGeneration.from_pretrained(model_name)
+
+def summarize_text(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="longest")
+    summary_ids = model.generate(
+        inputs["input_ids"],
+        max_length=150,
+        min_length=30,
+        length_penalty=2.0,
+        num_beams=4,
+        early_stopping=True
+    )
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary
+
+st.title("Text Summarization with Fine-Tuned Model")
+st.write("Enter text to generate a summary using the fine-tuned summarization model.")
+
+text = st.text_area("Input Text", height=200)
+if st.button("Summarize"):
+    if text:
+        with st.spinner("Summarizing..."):
+            summary = summarize_text(text)
+            st.success("Summary Generated")
+            st.write(summary)
+    else:
+        st.warning("Please enter some text to summarize.")
+
+if _name_ == "_main_":
+    st.set_option('deprecation.showfileUploaderEncoding', False)
+    st.markdown(
+        """
+        <style>
+        .reportview-container {
+            flex-direction: row;
+            justify-content: center.
+        }
+        </style>
+        """,
+        unsafe_allow_html=True
+    )