adapted demo model a little more

app.py

@@ -1,34 +1,3 @@
 import gradio as gr
-import torch
-from transformers import AutoTokenizer
-from model import SentimentClassifier
 
-model_state_dict = torch.load('sentimentality.h5')
-model = SentimentClassifier(2)
-model.load_state_dict(model_state_dict)
-model.eval()
-
-tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
-
-
-def preprocess(text):
-    inputs = tokenizer(text, padding='max_length',
-                       truncation=True, max_length=512, return_tensors='pt')
-    return inputs
-# Define a function to use the model to make predictions
-def predict(review):
-    inputs = preprocess(review)
-    with torch.no_grad():
-        outputs = model(inputs['input_ids'], inputs['attention_mask'])
-        predicted_class = torch.argmax(outputs[0]).item()
-    if(predicted_class==0):
-        return "It was a negative review"
-    return "It was a positive review"
-
-# Create a Gradio interface
-input_text = gr.inputs.Textbox(label="Input Text")
-output_text = gr.outputs.Textbox(label="Output Text")
-interface = gr.Interface(fn=predict, inputs=input_text, outputs=output_text)
-
-# Run the interface
-interface.launch()
+gr.Interface.load("models/finiteautomata/bertweet-base-sentiment-analysis",interpretation="default").launch()