Create app.py

app.py

@@ -0,0 +1,65 @@
+from transformers import T5ForConditionalGeneration,T5Tokenizer
+from transformers import AutoModelWithLMHead, AutoTokenizer
+from transformers import pipeline
+import streamlit as st 
+
+model = T5ForConditionalGeneration.from_pretrained("Michau/t5-base-en-generate-headline")
+tokenizer = T5Tokenizer.from_pretrained("Michau/t5-base-en-generate-headline")
+
+mrm_tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-summarize-news")
+mrm_model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-summarize-news")
+
+
+def generate_title(article):
+    text =  "headline: " + article
+    encoding = tokenizer.encode_plus(text, return_tensors = "pt", max_length=2048, truncation=True)
+    input_ids = encoding["input_ids"]
+    attention_masks = encoding["attention_mask"]
+
+    beam_outputs = model.generate(
+        input_ids = input_ids,
+        attention_mask = attention_masks,
+        max_length = 50,
+        num_beams = 3,
+        do_sample = True,
+        top_k=10,
+        early_stopping = False,
+    )
+
+    return tokenizer.decode(beam_outputs[0])
+
+# def generate_summary(article):
+#   input_ids = mrm_tokenizer.encode(article, return_tensors="pt", add_special_tokens=True)
+
+#   generated_ids = mrm_model.generate(input_ids=input_ids, num_beams=3, max_length=200,  repetition_penalty=2.5, length_penalty=1.0, early_stopping=False, truncation=True)
+
+#   preds = [mrm_tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]
+
+#   return preds[0]
+def generate_summary(article):
+    article = article[:1024]
+    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+    return summarizer(article, max_length=130, min_length=30, do_sample=False)
+def main():
+    st.title("Text Summarization")
+    text = st.text_area("Enter your text here:", "")
+
+    if st.button("Generate Summary"):
+        if text.strip() == "":
+            st.error("Please enter some text.")
+        else:
+            title = generate_title(text)
+            summary = generate_summary(text)
+            # summary = summary[0]['summary_text']
+
+            st.subheader("Generated Title:")
+            st.write(title.replace('<pad>', '').replace('</s>', ''))
+            
+            st.subheader("Generated Description:")
+
+            # st.write(summary.replace('<pad>', '').replace('</s>', ''))
+            st.write(summary[0]['summary_text'])
+
+
+if __name__ == "__main__":
+    main()