Create app.py

app.py

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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()