Create app.py

app.py

@@ -0,0 +1,64 @@
+import gradio as gr
+import numpy as np
+import random
+import re
+import torch
+import transformers
+
+from keybert import KeyBERT
+from transformers import (T5ForConditionalGeneration, T5Tokenizer)
+
+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 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 = []
+    print(slide)
+    print(orig)
+    print(ap)
+    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]
+    print("first",outputs)
+    print(temp)
+
+    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"))
+    print("second",outputs)
+
+    return outputs
+
+gr.Interface(func, [gr.inputs.Textbox(lines=10, label="context"), gr.inputs.Slider(minimum=1, maximum=5, default=1, label="No of Question"),], gr.outputs.KeyValues()).launch()