app.py

from transformers import AutoTokenizer, AutoModelForQuestionAnswering
import torch

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")
    answer_start_scores, answer_end_scores = model(**inputs).values()

    start = torch.argmax(answer_start_scores)
    end = torch.argmax(answer_end_scores) + 1

    answer = tokenizer.convert_tokens_to_string(
        tokenizer.convert_ids_to_tokens(inputs["input_ids"][0][start:end])
    )
    return answer
import gradio as gr

def chatbot_response(question):
    context = "Include a reliable medical knowledge base here like PubMed abstracts or simplified texts."
    return answer_question(context, question)

iface = gr.Interface(fn=chatbot_response, inputs="text", outputs="text", title="Medical Chatbot")
iface.launch()