Create app.py

app.py

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+import pickle
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from sentence_transformers import SentenceTransformer
+import lightgbm
+lr_clf_finbert = pickle.load(open("lr_clf_finread_new.pkl",'rb'))
+model_read = SentenceTransformer('ProsusAI/finbert')
+
+def get_readability(text):
+  emd = model_read.encode([text])
+  ans = 'not readable'
+  if lr_clf_finbert.predict(emd)==1:
+    ans = 'readable'
+  score = round(lr_clf_finbert.predict_proba(emd)[0,1],4)
+  return score
+
+# Reference : https://huggingface.co/humarin/chatgpt_paraphraser_on_T5_base
+tokenizer = AutoTokenizer.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base")
+model = AutoModelForSeq2SeqLM.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base")
+
+def paraphrase(
+    question,
+    num_beams=5,
+    num_beam_groups=5,
+    num_return_sequences=5,
+    repetition_penalty=10.0,
+    diversity_penalty=3.0,
+    no_repeat_ngram_size=2,
+    temperature=0.7,
+    max_length=128
+):
+    input_ids = tokenizer(
+        f'paraphrase: {question}',
+        return_tensors="pt", padding="longest",
+        max_length=max_length,
+        truncation=True,
+    ).input_ids
+    
+    outputs = model.generate(
+        input_ids, temperature=temperature, repetition_penalty=repetition_penalty,
+        num_return_sequences=num_return_sequences, no_repeat_ngram_size=no_repeat_ngram_size,
+        num_beams=num_beams, num_beam_groups=num_beam_groups,
+        max_length=max_length, diversity_penalty=diversity_penalty
+    )
+
+    res = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+    return res
+
+def get_most_raedable_paraphrse(text):
+  li_paraphrases = paraphrase(text)
+  li_paraphrases.append(text)
+  best = li_paraphrases[0]
+  score_max = get_readability(best)
+  for i in range(1,len(li_paraphrases)):
+    curr = li_paraphrases[i]
+    score = get_readability(curr)
+    if score > score_max:
+      best = curr
+      score_max = score
+  if best!=text and score_max>.6:
+    ans = "The most redable version of text that I can think of is:\n" + best  
+  else:
+    "Sorry! I am not confident. As per my best knowledge, you already have the most readable version of the text!"
+  return ans
+
+def set_example_text(example_text):
+    return gr.Textbox.update(value=example_text[0])
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+    """
+    # FinLanSer
+    Financial Language Simplifier
+    """)
+    text = gr.Textbox(label="Enter text you want to simply (make more readable)")
+    greet_btn = gr.Button("Simplify/Make Readable")
+    output = gr.Textbox(label="Output Box")
+    greet_btn.click(fn=get_most_raedable_paraphrse, inputs=text, outputs=output, api_name="get_most_raedable_paraphrse")
+    example_text = gr.Dataset(components=[text], samples=[['Inflation is the rate of increase in prices over a given period of time. Inflation is typically a broad measure, such as the overall increase in prices or the increase in the cost of living in a country.'], ['Legally assured line of credit with a bank'], ['A mutual fund is a type of financial vehicle made up of a pool of money collected from many investors to invest in securities like stocks, bonds, money market instruments']])
+    example_text.click(fn=set_example_text, inputs=example_text,outputs=example_text.components)  
+
+demo.launch()