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def highlight_answer(context, answer):
"""
Highlight the answer in the given context.
Parameters:
- context (str): The context in which the answer is found.
- answer (str): The answer to be highlighted.
Returns:
- str: The context with the answer highlighted by '<h>' tags.
Example:
>>> context = 'The quick brown fox jumps over the lazy dog.'
>>> answer = 'fox'
>>> highlight_answer(context, answer)
'The quick brown <h> fox <h> jumps over the lazy dog.'
"""
context_splits = context.split(answer)
text = ""
for split in context_splits:
text += split
text += ' <h> '
text += answer
text += ' <h> '
text += split
return text
def prepare_instruction(answer_highlighted_context):
"""
Prepare an instruction prompt for generating a question.
Parameters:
- answer_highlighted_context (str): The context with the answer highlighted by '<h>' tags.
Returns:
- str: The instruction prompt string.
Example:
>>> answer_highlighted_context = 'The quick brown <h> fox <h> jumps over the lazy dog.'
>>> prepare_instruction(answer_highlighted_context)
'Generate a question whose answer is highlighted by <h> from the context delimited by the triple backticks.\\n context:\\n ```\\n The quick brown <h> fox <h> jumps over the lazy dog.\\n ```\\n '
"""
instruction_prompt = f"""Generate a question whose answer is highlighted by <h> from the context delimited by the triple backticks.
context:
```
{answer_highlighted_context}
```
"""
return instruction_prompt
from transformers import pipeline
pipe = pipeline('text2text-generation', model='mohammedaly2222002/t5-small-squad-qg-v2', device_map='auto')
import gradio as gr
def processed(question,answer,num):
# The uploaded image is a PIL image
answer_highlighted_context = highlight_answer(context=question, answer=answer)
prompt = prepare_instruction(answer_highlighted_context)
outputs = pipe(prompt,num_return_sequences=int(num),num_beams=5,num_beam_groups=5,diversity_penalty=1.0)
result="Generated questions are "+"\n"+"\n"
number=0
for output in outputs:
number=number+1
result+=str(number)+"."+output['generated_text']+"\n"
return result
iface = gr.Interface(processed, # Function to process the image
inputs=[
gr.Textbox(label="Question"),
gr.Textbox(label="Answer"),
gr.Textbox(label="Numbers of question")],
outputs="text" # Image output
)
iface.launch() |