Update app.py

app.py

@@ -1,51 +1,19 @@
+# here are some examples for sadness, joy, anger, and optimism.
 import gradio as gr
-from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
 
-class EmotionClassifier:
-    def __init__(self, model_name: str):
-        self.model = AutoModelForSequenceClassification.from_pretrained(model_name)
-        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
-        self.pipeline = pipeline(
-            "text-classification",
-            model=self.model,
-            tokenizer=self.tokenizer,
-            return_all_scores=True,
-        )
+model = "models/barbieheimer/MND_TweetEvalBert_model"
 
+def predict(prompt):
+    completion = classifier(prompt)
+    return completion[0]["label"], completion[0]["score"]
 
-    def predict(self, input_text: str):
-        pred = self.pipeline(input_text)[0]
-        result = {
-            "Anger 😠": pred[0]["score"],
-            "Joy 😂": pred[1]["score"],
-            "Optimism 😍": pred[2]["score"],
-            "Anger 😭": pred[3]["score"],
-        }
-        return result
+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...."]
+]
 
-
-def main():
-    model = EmotionClassifier("models/barbieheimer/MND_TweetEvalBert_model")
-    iface = gr.Interface(
-        fn=model.predict,
-        inputs=gr.inputs.Textbox(
-            lines=3,
-            placeholder="Type a phrase that has some emotion",
-            label="Input Text",
-        ),
-        outputs="label",
-        title="Emotion Classification",
-        examples=[
-            "I get so down when I'm alone",
-            "I believe that today everything will work out",
-            "It was so dark there I was afraid to go",
-            "I loved the gift you gave me",
-            "I was very surprised by your presentation.",
-        ],
-    )
-
-    iface.launch()
-
-
-if __name__ == "__main__":
-    main()
+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()