Create app.py

app.py

@@ -0,0 +1,75 @@
+import streamlit as st
+
+from transformers import pipeline
+from transformers import PegasusForConditionalGeneration, PegasusTokenizer
+
+
+def summarize(data, modelname):
+    if (modelname == 'Bart'):
+        summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+        print("world")
+        output = summarizer(data, max_length=130, min_length=30, do_sample=False)
+        return output[0]["summary_text"]
+    elif (modelname == 'Pegasus'):
+        model = PegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum")
+        tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-xsum")
+
+        # Create tokens - number representation of our text
+        tokens = tokenizer(data, truncation=True, padding="longest", return_tensors="pt")
+        summary = model.generate(**tokens)
+        return tokenizer.decode(summary[0])
+
+
+st.sidebar.title("Text Summarization")
+
+uploaded_file = st.sidebar.file_uploader("Choose a file")
+data = ""
+output = ""
+if uploaded_file is not None:
+    # To read file as bytes:
+    bytes_data = uploaded_file.getvalue()
+
+    data = bytes_data.decode("utf-8")
+modelname = st.sidebar.radio("Choose your model",
+                             ["Bart", "Pegasus"],
+                             help=" you can choose between 2 models (Bart or Pegasus) to summarize your text. More to come!", )
+col1, col2 = st.columns(2)
+
+with col1:
+    st.header("Copy paste your text or Upload file")
+    if (uploaded_file is not None):
+        with st.expander("Text to summarize", expanded=True):
+            st.write(
+                data
+            )
+    else:
+        with st.expander("Text to summarize", expanded=True):
+            data = st.text_area("Paste your text below (max 500 words)", height=510, )
+
+        MAX_WORDS = 500
+        import re
+
+        res = len(re.findall(r"\w+", data))
+        if res > MAX_WORDS:
+            st.warning(
+                "⚠️ Your text contains "
+                + str(res)
+                + " words."
+                + " Only the first 500 words will be reviewed. Stay tuned as increased allowance is coming! 😊")
+        data = data[:MAX_WORDS]
+    Summarizebtn = st.button("Summarize")
+    if (Summarizebtn):
+        output = summarize(data, modelname)
+
+with col2:
+    st.header("Summary")
+    if (len(output) > 0):
+        with st.expander("", expanded=True):
+            st.write(output)
+
+
+
+
+
+
+