import json
import gradio as gr
import pandas as pd
from transformers import AutoTokenizer
import torch
from fastai.learner import load_learner

df=pd.read_csv('healifyLLM_answer_dataset.csv')

with open('question_labels.json', 'r') as f:
    questions_label = json.load(f)
que_classes = list(questions_label.keys())

def answering(text):
  learner_inf = load_learner(fname="healifyLLM-stage4.pkl")
  percentage = learner_inf.blurr_predict(text)[0]['score']* 100
  index = learner_inf.blurr_predict(text)[0]['class_index']
  label = learner_inf.blurr_predict(text)[0]['class_labels'][index]
  result = df[df['label'] == label]['answer']
  if percentage >= 35:
    return result.iloc[0]
  else:
    return "My knowledge is limited. Ask some other medical question."

label = gr.components.Label(label="Answer")
iface = gr.Interface(fn=answering,inputs="text", outputs=label, title="Disease QnA")
iface.launch(inline=False)