import gradio as gr from transformers import AutoTokenizer import torch, json from fastai.text.all import * from blurr.text.modeling.all import * #from blurr.text.data.all import * # from blurr.modeling.core import Blearner # learner = Blearner.load_learner('path/to/your/export.pkl') # result = learner.blurr_predict('Your text here') with open('question_labels.json', 'r') as f: question_dictionary = json.load(f) que_classes = list(question_dictionary.keys()) blurr_model = load_learner('healifyLLM-stage4.pkl') def detect_question(text): # research tokenization requirement for blurr_predict() probs = blurr_model.blurr_predict(text)[0]['probs'] return dict(zip(que_classes, map(float, probs))) label = gr.outputs.Label(num_top_classes=5) #interface with i/o iface = gr.Interface(fn=detect_question, inputs="text", outputs=label) iface.launch(inline=False)