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import gradio as gr | |
from transformers import pipeline | |
from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
import torch | |
tokenizer = AutoTokenizer.from_pretrained("FuriouslyAsleep/unhappyZebra100") | |
model = AutoModelForSequenceClassification.from_pretrained("FuriouslyAsleep/unhappyZebra100") | |
def greet(Doc_Passage_To_Test): | |
inputs = tokenizer(Doc_Passage_To_Test, return_tensors="pt") | |
outputs = model(**inputs) | |
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) | |
#print(predictions) | |
# return "Hello " + name + "!!" | |
return "Probabilities are listed here (False prob, then True prob): " + str(predictions) | |
iface = gr.Interface(fn=greet, inputs="text", outputs="text") | |
iface.launch() | |