Upload app.py

app.py

@@ -2,6 +2,7 @@ import numpy as np
 import pandas as pd 
 import re
 import torch
+import gradio as gr
 
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
@@ -14,7 +15,7 @@ model_gen_title = AutoModelForSeq2SeqLM.from_pretrained("Ateeqq/news-title-gener
 def generate_title(input_text): #Generate a title for input text with Ateeq model
 
   input_ids = tokenizer_gen_title.encode(input_text, return_tensors="pt") #Tokenize input text
-  input_ids = input_ids.to('cuda') #Send tokenized inputs to gpu
+  #input_ids = input_ids.to('cuda') #Send tokenized inputs to gpu
   output = model_gen_title.generate(input_ids, 
                           max_new_tokens=100, 
                           do_sample=True, 
@@ -34,18 +35,19 @@ def split_into_sentences(paragraph): #For paraphraser - return a list of sentenc
     return sentences
 
 def paraphrase(
-    question,
+    text,
+    beam_search,
     #num_beams=10,
     #num_beam_groups=10,
     #num_return_sequences=1,
     #repetition_penalty=1.0,
     #diversity_penalty=1.0,
-    no_repeat_ngram_size=3,
+    #no_repeat_ngram_size=3,
     temperature=0.8,
     max_length=128
 ):
-    
-    sentence_list = split_into_sentences(question) #feed input para into sentence splitter
+ if text != "":
+    sentence_list = split_into_sentences(text) #feed input para into sentence splitter
     output = [] #List to hold the individual rephrased sentences obtained from the model
 
     for sentence in sentence_list:
@@ -60,23 +62,37 @@ def paraphrase(
         outputs = model.generate(
             input_ids,
             do_sample=True,
+            num_beams = 20 if beam_search else 1,
             temperature=temperature,
             max_length=max_length, 
-            no_repeat_ngram_size=no_repeat_ngram_size
+            no_repeat_ngram_size=4
         )
     
         res = tokenizer.batch_decode(outputs, skip_special_tokens=True)
         output.append(res[0]) #Add rephrased sentence to list
 
     paraphrased_text = "" #to hold the combined sentence output made from generated list
+    titles_list = "" #to hold the three titles
 
     for sentence in output: #Join all new reworded sentences together
-        paraphrased_text += sentence + " "   
-
+        paraphrased_text += sentence + " "
+     
     for title in range (1,4): #Print 3 titles by calling Ateeq model fn - generate_title
-        print (f"Title {title}: {generate_title (paraphrased_text)}")
-        print ("")
+        
+        titles_list += (f"Title {title}: {generate_title (paraphrased_text)}<br>")
+        #titles_list.append ("") #space after each title
     
-    return paraphrased_text # Return paraphrased text after printing three titles above
+    
+    return (titles_list, paraphrased_text) # Return paraphrased text after printing three titles above
+
 
+iface = gr.Interface(fn=paraphrase, 
+                     inputs=[gr.Textbox(label="Paste text in the input box and press 'Submit'.", lines=10), "checkbox", gr.Slider(0.1, 2, 0.8)],
+                     outputs=[gr.HTML(label="Titles:"), gr.Textbox(label="Rephrased text:", lines=15)],
+                     title="AI Paraphraser with Title Generator", 
+                     description="Sentencet-to-sentence rewording backed with GPT-3.5 training set",
+                     article="<div align=left><h1>AI Paraphraser and Title Generator</h1><li>Each sentence is rephrased separately without context.</li><li>Temperature: Increase value for more creative rewordings. Higher values may corrupt the sentence. Reset value after pressing 'Clear'</li><li>Beam search: Try for safer and conservative rephrasing.</li><p>Models:<br><li>Training set derived by using Chat-GPT3.5. No competition intended.</li><li>Original models: humarin/chatgpt_paraphraser_on_T5_base and Ateeq_news_title_generator. Deployment code modified for long text inputs.</li></p><p>Parameter details:<br><li>For rephraser: Beam search: No. of beams = 20, no_repeat_ngram_size=4, do_sample=True.</li><li>For title generator: do_sample=True, temperature=0.8, top_k = 20 </li></div>",
+                     flagging_mode='never'
+                    )
 
+iface.launch()