Initial Commit

app.py

@@ -0,0 +1,106 @@
+import gradio as gr
+import numpy as np
+from keybert import KeyBERT
+import random
+from transformers import (
+    T5ForConditionalGeneration,
+    T5Tokenizer,
+)
+import re
+import transformers
+import torch
+
+
+DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+MAX_LEN = 512
+
+tokenizer = T5Tokenizer.from_pretrained('t5-base')
+model = T5ForConditionalGeneration.from_pretrained(
+    'Vaibhavbrkn/question-gen')
+mod = KeyBERT('distilbert-base-nli-mean-tokens')
+model.to(DEVICE)
+
+context = "The Transgender Persons Bill, 2016 was hurriedly passed in the Lok Sabha, amid much outcry from the very community it claims to protect."
+
+
+def filter_keyword(data, ran=5):
+    ap = []
+    real = []
+    res = re.sub(r'-', ' ', data)
+    res = re.sub(r'[^\w\s\.\,]', '', res)
+    for i in range(1, 4):
+        ap.append(mod.extract_keywords(
+            res, keyphrase_ngram_range=(1, i), diversity=0.7, top_n=ran*2))
+    for i in range(3):
+        for j in range(len(ap[i])):
+            if ap[i][j][0].lower() in res.lower():
+                real.append(ap[i][j])
+
+    real = sorted(real, key=lambda x: x[1], reverse=True)
+    ap = []
+    st = ""
+    for i in range(len(real)):
+        if real[i][0] in st:
+            continue
+        else:
+            ap.append(real[i])
+            st += real[i][0] + " "
+        if len(ap) == ran:
+            break
+
+    return ap
+
+
+# FOR BAD label negative or bottom 3
+
+def func(context, slide):
+    slide = int(slide)
+    randomness = 0.4
+    orig = int(np.ceil(randomness * slide))
+    temp = slide - orig
+    ap = filter_keyword(context, ran=slide*2)
+    outputs = []
+    for i in range(orig):
+
+        inputs = "context: " + context + " keyword: " + ap[i][0]
+        source_tokenizer = tokenizer.encode_plus(
+            inputs, max_length=512, pad_to_max_length=True, return_tensors="pt")
+        outs = model.generate(input_ids=source_tokenizer['input_ids'].to(
+            DEVICE), attention_mask=source_tokenizer['attention_mask'].to(DEVICE), max_length=50)
+        dec = [tokenizer.decode(ids) for ids in outs][0]
+        st = dec.replace("<pad> ", "")
+        st = st.replace("</s>", "")
+        if ap[i][1] > 0.0:
+            outputs.append((st, "Good"))
+        else:
+            outputs.append((st, "Bad"))
+
+    del ap[: orig]
+
+    if temp > 0:
+        for i in range(temp):
+            keyword = random.choice(ap)
+            inputs = "context: " + context + \
+                " keyword: " + keyword[0]
+            source_tokenizer = tokenizer.encode_plus(
+                inputs, max_length=512, pad_to_max_length=True, return_tensors="pt")
+            outs = model.generate(input_ids=source_tokenizer['input_ids'].to(
+                DEVICE), attention_mask=source_tokenizer['attention_mask'].to(DEVICE), max_length=50)
+            dec = [tokenizer.decode(ids) for ids in outs][0]
+            st = dec.replace("<pad> ", "")
+            st = st.replace("</s>", "")
+            if keyword[1] > 0.0:
+                outputs.append((st, "Good"))
+            else:
+                outputs.append((st, "Bad"))
+
+    return outputs
+
+
+gr.Interface(func,
+             [
+                 gr.inputs.Textbox(lines=10, label="context"),
+                 gr.inputs.Slider(minimum=1, maximum=5,
+                                  default=3, label="No of Question"),
+             ],
+             gr.outputs.KeyValues(), capture_session=True, server_name="0.0.0.0").launch()

requirements.txt

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+numpy
+torch
+transformers
+keybert
+gradio