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from transformers import AutoTokenizer, AutoModelForQuestionAnswering
import torch
import gradio as gr
tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
model = AutoModelForQuestionAnswering.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
def answer_question(context, question):
inputs = tokenizer.encode_plus(question, context, return_tensors="pt")
outputs = model(**inputs)
start_scores = outputs.start_logits
end_scores = outputs.end_logits
start = torch.argmax(start_scores)
end = torch.argmax(end_scores) + 1
if start >= end:
return "I couldn't find an answer."
answer = tokenizer.convert_tokens_to_string(
tokenizer.convert_ids_to_tokens(inputs["input_ids"][0][start:end])
)
return answer
def chatbot_response(question):
context = (
"COVID-19 is a respiratory illness caused by the SARS-CoV-2 virus. "
"Common symptoms include fever, cough, fatigue, and loss of taste or smell. "
"Fever usually lasts for 3-5 days. Treatment is mostly supportive, and vaccination reduces severity."
)
return answer_question(context, question)
iface = gr.Interface(fn=chatbot_response, inputs="text", outputs="text", title="Medical Chatbot")
iface.launch()
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