Update app.py

app.py

@@ -13,35 +13,33 @@ tokenizer.do_lower_case = True
 model = AutoModelForSequenceClassification.from_pretrained("guidecare/feelings_and_issues_large_v2", token=authtoken, use_safetensors=True)
 all_label_names = list(model.config.id2label.values())
 
-
 def predict(text):
     probs = expit(model(**tokenizer([text], return_tensors="pt", padding=True)).logits.detach().numpy())
-    # can't use numpy for whatever reason
     probs = [float(np.round(i, 2)) for i in probs[0]]
-    # break out issue, harm, sentiment, feeling
     zipped_list = list(zip(all_label_names, probs))
     print(text, zipped_list)
+    
     issues = [(i, j) for i, j in zipped_list if i.startswith('issue')]
     feelings = [(i, j) for i, j in zipped_list if i.startswith('feeling')]
     harm = [(i, j) for i, j in zipped_list if i.startswith('harm')]
     sentiment = [(i, j) for i, j in zipped_list if i.startswith('sentiment')]
-    # keep tops for each one
-    issues = sorted(issues, key=lambda x: x[1])[::-1]
-    feelings = sorted(feelings, key=lambda x: x[1])[::-1]
-    harm = sorted(harm, key=lambda x: x[1])[::-1]
-    sentiment = sorted(sentiment, key=lambda x: x[1])[::-1]
-    # top is the combo of these
+    
+    issues = sorted(issues, key=lambda x: x[1], reverse=True)
+    feelings = sorted(feelings, key=lambda x: x[1], reverse=True)
+    harm = sorted(harm, key=lambda x: x[1], reverse=True)
+    sentiment = sorted(sentiment, key=lambda x: x[1], reverse=True)
+    
     top = issues + feelings + harm + sentiment
-
     d = {i: j for i, j in top}
     return d
 
-
 iface = gr.Interface(
-  fn=predict, 
-  inputs="text",
-  outputs="label",
-  #examples=["This test tomorrow is really freaking me out."]
+    fn=predict,
+    inputs=gr.Textbox(label="Enter text"),
+    outputs=gr.Label(label="Predictions"),
+    title="Emotion and Issue Predictor",
+    description="Enter a text to predict emotions and issues.",
 )
 
-iface.launch()
+if __name__ == "__main__":
+    iface.launch()