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| from transformers import AutoModelForQuestionAnswering, AutoTokenizer | |
| import torch | |
| import gradio as gr | |
| model = AutoModelForQuestionAnswering.from_pretrained("msures3/distilbert-base-squad") | |
| tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") | |
| def generate_response(context, question): | |
| inputs = tokenizer(question, context, return_tensors="pt") | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| answer_start_index = outputs.start_logits.argmax() | |
| answer_end_index = outputs.end_logits.argmax() | |
| predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1] | |
| predicted_answers = tokenizer.decode(predict_answer_tokens) | |
| return predicted_answers | |
| inputs = [ | |
| gr.Textbox(label="Enter Context"), | |
| gr.Textbox(label="Enter Question") | |
| ] | |
| outputs = gr.Textbox(label="Predicted Answer") | |
| app = gr.Interface( | |
| fn=generate_response, | |
| inputs=inputs, | |
| outputs=outputs, | |
| title="Context Based Question Answering", | |
| description="Enter a context and a question to get the predicted answer.", | |
| examples=[ | |
| ["This is a sample context. The quick brown fox jumps over the lazy dog.", "What animal jumps over the dog?"], | |
| ["The capital of France is Paris. It is a beautiful city with many attractions.", "What is the capital of France?"] | |
| ] | |
| ) | |
| app.launch() |