Update app.py

app.py

@@ -1,27 +1,66 @@
+# 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 = 'facebook/bart-large-cnn'
+
+# 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
+#     )
 import os
 os.system('pip install streamlit transformers torch')
 
 import streamlit as st
-from transformers import BartTokenizer, BartForConditionalGeneration
+from transformers import pipeline
 
-# Load the model and tokenizer
-model_name = 'facebook/bart-large-cnn'
-
-tokenizer = BartTokenizer.from_pretrained(model_name)
-model = BartForConditionalGeneration.from_pretrained(model_name)
+# Load the summarization pipeline
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
 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
+    summary = summarizer(text, max_length=150, min_length=30, length_penalty=2.0, num_beams=4, early_stopping=True)
+    return summary[0]['summary_text']
 
 st.title("Text Summarization with Fine-Tuned Model")
 st.write("Enter text to generate a summary using the fine-tuned summarization model.")