AneriThakkar
commited on
Commit
•
2a0d6cc
1
Parent(s):
2661a89
Upload main.py
Browse files
main.py
ADDED
@@ -0,0 +1,58 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# import torch
|
2 |
+
import streamlit as st
|
3 |
+
# import numpy as np
|
4 |
+
from transformers import T5ForConditionalGeneration, T5Tokenizer
|
5 |
+
# from transformers import pipeline
|
6 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
|
7 |
+
|
8 |
+
def load_model(model_name):
|
9 |
+
if model_name == "T5":
|
10 |
+
model = T5ForConditionalGeneration.from_pretrained('google/flan-t5-base')
|
11 |
+
tokenizer = T5Tokenizer.from_pretrained('google/flan-t5-base')
|
12 |
+
return model, tokenizer
|
13 |
+
if model_name == "Llama3":
|
14 |
+
model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B")
|
15 |
+
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B")
|
16 |
+
return model, tokenizer
|
17 |
+
if model_name == "Llama3-Instruct":
|
18 |
+
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
|
19 |
+
model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
|
20 |
+
return model, tokenizer
|
21 |
+
else:
|
22 |
+
st.error(f"Model {model_name} not available.")
|
23 |
+
return None, None
|
24 |
+
|
25 |
+
def generate_question(model,tokenizer,context):
|
26 |
+
input_text = 'Generate a question from this: ' + context
|
27 |
+
input_ids = tokenizer(input_text, return_tensors='pt').input_ids
|
28 |
+
outputs = model.generate(input_ids,max_length=512)
|
29 |
+
output_text = tokenizer.decode(outputs[0][1:len(outputs[0])-1])
|
30 |
+
return output_text
|
31 |
+
|
32 |
+
def main():
|
33 |
+
st.title("Question Generation From Given Text")
|
34 |
+
context = st.text_area("Enter text","Laughter is the best medicine.")
|
35 |
+
st.write("Select a model and provide the text to generate questions.")
|
36 |
+
model_choice = st.selectbox("Select a model", ["T5", "Llama3", "Llama3-Instruct"])
|
37 |
+
|
38 |
+
if st.button("Generate Questions"):
|
39 |
+
model, tokenizer = load_model(model_choice)
|
40 |
+
if model and tokenizer:
|
41 |
+
questions = generate_question(model, tokenizer, context)
|
42 |
+
st.write("Generated Question:")
|
43 |
+
st.write(questions)
|
44 |
+
else:
|
45 |
+
st.error("Model loading failed.")
|
46 |
+
# tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base")
|
47 |
+
# model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base")
|
48 |
+
# tokenizer = AutoTokenizer.from_pretrained("ramsrigouthamg/t5_squad_v1")
|
49 |
+
# model = AutoModelForSeq2SeqLM.from_pretrained("ramsrigouthamg/t5_squad_v1")
|
50 |
+
# input_text = 'Generate a question from this: ' + context
|
51 |
+
# input_ids = tokenizer(input_text, return_tensors='pt').input_ids
|
52 |
+
# outputs = model.generate(input_ids)
|
53 |
+
# output_text = tokenizer.decode(outputs[0][1:len(outputs[0])-1])
|
54 |
+
# st.write("Generated question:")
|
55 |
+
# st.write(output_text)
|
56 |
+
|
57 |
+
if __name__ == '__main__':
|
58 |
+
main()
|