Create app.py

app.py

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+# import torch
+# from transformers import PegasusForConditionalGeneration, PegasusTokenizer
+
+# model_name = 'tuner007/pegasus_paraphrase'
+# torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
+# tokenizer = PegasusTokenizer.from_pretrained(model_name)
+# model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
+
+# def get_response(input_text,num_return_sequences):
+#   batch = tokenizer.prepare_seq2seq_batch([input_text],truncation=True,padding='longest',max_length=60, return_tensors="pt").to(torch_device)
+#   translated = model.generate(**batch,max_length=60,num_beams=10, num_return_sequences=num_return_sequences, temperature=1.5)
+#   tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
+#   return tgt_text
+
+# from sentence_splitter import SentenceSplitter, split_text_into_sentences
+
+# splitter = SentenceSplitter(language='en')
+
+# def paraphraze(text):
+#   sentence_list = splitter.split(text)
+#   paraphrase = []
+
+#   for i in sentence_list:
+#     a = get_response(i,1)
+#     paraphrase.append(a)
+#     paraphrase2 = [' '.join(x) for x in paraphrase]
+#     paraphrase3 = [' '.join(x for x in paraphrase2) ]
+#   paraphrased_text = str(paraphrase3).strip('[]').strip("'")
+#   return paraphrased_text
+
+
+# def summarize(text):
+
+#   paraphrased_text = paraphraze(text)
+#   return paraphrased_text
+
+
+import streamlit as st
+from paraphraser import get_paraphrased_sentences, model, tokenizer
+
+
+def app():
+    st.title('Paraphraser')
+    st.write('Please provide the text to be paraphrased')
+    col1, spacer, col2 = st.columns([3,1,3]) #st.beta_columns((2,1,1,1))
+
+    x = 0
+    output = ['Result ']
+    with col1:
+        user_input = st.text_area('Enter text','', height=200)
+        
+        paraphraseNo = st.slider('Number of Parapharases',1,10,2)
+        if st.button('Paraphrase'):
+            with st.spinner(text="This may take a moment..."):
+                output = get_paraphrased_sentences(model, tokenizer, user_input, num_beams=10, num_return_sequences=paraphraseNo)    
+    
+    #with spacer:
+    
+    with col2:
+        for x, element in enumerate(output):
+            user_output = st.text_area(label="", value=output[x], height=150 )