app.py

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyOewSj8PHA49VeNmaZxMZGo"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"SPAkgiSviyO0"},"outputs":[],"source":["import gradio as gr\n","import torch\n","from transformers import AutoModelForSequenceClassification, AutoTokenizer\n","\n","# 모델 경로 설정\n","model_path = \"./model\"  # 업로드된 모델 디렉토리 경로\n","\n","# 모델과 토크나이저 로드\n","model = AutoModelForSequenceClassification.from_pretrained(model_path)\n","tokenizer = AutoTokenizer.from_pretrained(\"klue/bert-base\")\n","\n","# 예측 함수\n","def predict(text):\n","    inputs = tokenizer(text, return_tensors=\"pt\")\n","    outputs = model(**inputs)\n","    probabilities = torch.sigmoid(outputs.logits)\n","    depression_prob = probabilities[0, 1].item()\n","\n","    if depression_prob > 0.5:\n","        return f\"Depressed (Confidence: {depression_prob:.2%})\"\n","    else:\n","        return f\"Not Depressed (Confidence: {1 - depression_prob:.2%})\"\n","\n","# Gradio 인터페이스\n","interface = gr.Interface(\n","    fn=predict,\n","    inputs=gr.Textbox(label=\"Enter your text here\"),\n","    outputs=gr.Textbox(label=\"Result\"),\n","    title=\"Depression Detection\",\n","    description=\"Predict the likelihood of depression based on text input.\",\n",")\n","\n","interface.launch()\n"]}]}