| import pickle | |
| import numpy as np | |
| import gradio as gr | |
| # install transformers and torch in requirements.txt | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| model = pickle.load(open("model.pkl", "rb")) | |
| vectorizer = pickle.load(open("vectorizer.pkl", "rb")) | |
| def classify_text(inp): | |
| new_question_vector = vectorizer.transform([inp]) | |
| prediction = model.predict(new_question_vector) | |
| return str(prediction[0]) | |
| iface = gr.Interface(fn=classify_text, inputs="text", outputs="label",title="Tabibu Bot", | |
| interpretation="default", examples=[ | |
| ["I am feeling depressed"], | |
| ["I am feeling anxious"], | |
| ["I am feeling stressed"], | |
| ["I am feeling sad"], | |
| ]) | |
| iface.launch() |