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import gradio as gr
from course_content import (
course_introduction,
how_language_model_works,
practical_application_and_usecases_of_llms,
what_is_generative_ai,
llms_for_writing,
llms_for_reading,
llms_for_chatting,
llms_capabilities_limitations,
llms_image_generation
)
from simpletransformers.question_answering import QuestionAnsweringModel
# Initialize a Question Answering model
model = QuestionAnsweringModel(
"bert", # Specify the model type, e.g., 'bert', 'roberta', etc.
"bert-base-uncased", # Specify the model size and configuration
use_cuda=False # Set to True if you have CUDA enabled GPU
)
def get_answer(question):
input_data = [
{
"id": 0,
"context": llms_image_generation,
"qas": [{"question": question, "id": 0}]
}
]
# Perform prediction
results = model.predict(input_data)
# Assuming results is the output of model.predict(input_data)
answer_dict = results[0][0] # Get the dictionary containing answers and IDs
answer_segments = answer_dict['answer'] # Get the list of answer segments
probabilities = results[1][0]['probability'] # Get the list of probabilities
# Find the index of the answer segment with the highest probability
best_index = probabilities.index(max(probabilities))
best_answer = answer_segments[best_index]
return best_answer
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