Create app.py

app.py

@@ -1,26 +0,0 @@
-# here are some examples for sadness, joy, anger, and optimism.
-import gradio as gr
-# Load model directly
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-from transformers import pipeline
-
-tokenizer = AutoTokenizer.from_pretrained("barbieheimer/MND_TweetEvalBert_model")
-model = AutoModelForSequenceClassification.from_pretrained("barbieheimer/MND_TweetEvalBert_model")
-
-# We can now use the model in the pipeline.
-classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
-
-def predict(prompt):
-    completion = classifier(prompt)
-    return completion[0]["label"], completion[0]["score"]
-
-examples = [
-    ["The movie was a bummer."],
-    ["I cannot wait to watch all these movies!"],
-    ["The ending of the movie really irks me, gives me the ick fr."],
-    ["The protagonist seems to have a lot of hope...."]
-]
-
-gr.Interface.load("models/barbieheimer/MND_TweetEvalBert_model", fn=predict, title="Sentiment Analysis", examples=examples,
-    inputs=gr.inputs.Textbox(lines=5, label="Paste an Article here."),
-    outputs=[gr.outputs.Textbox(label="Label"),gr.outputs.Textbox(label="Score")],).launch()