| import gradio as gr | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| import torch | |
| import numpy as np | |
| model_name = "Bittar/outputs" | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| mapping = { | |
| 0: 'negative', | |
| 1: 'positive' | |
| } | |
| def predict(text): | |
| inputs = tokenizer(text, return_tensors="pt") | |
| outputs = model(**inputs) | |
| predictions = outputs.logits | |
| return mapping[predictions.argmax().item()] | |
| iface = gr.Interface( | |
| fn=predict, | |
| inputs="text", | |
| outputs="text", | |
| layout="vertical", | |
| title="Movie feelings classifier", | |
| description="do u feel positive or negative about a movie? Write your review down and find out! (only works in english)" | |
| ) | |
| iface.launch(share=True) |