Create app.py

app.py

@@ -0,0 +1,35 @@
+import streamlit as st
+from transformers import pipeline
+from transformers import AutoModelForSequenceClassification
+from transformers import AutoTokenizer
+import torch
+import numpy as np
+
+def main():
+
+
+    st.title("yelp2024fall Test")
+    st.write("Enter a sentence for analysis:")
+
+    user_input = st.text_input("")
+    if user_input:
+        # Approach: AutoModel
+        model2 = AutoModelForSequenceClassification.from_pretrained("isom5240/CustomModel_yelp2024fall",
+                                                                    num_labels=5)
+        tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+
+        inputs = tokenizer(user_input,
+                        padding=True,
+                        truncation=True,
+                        return_tensors='pt')
+
+        outputs = model2(**inputs)
+        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        predictions = predictions.cpu().detach().numpy()
+        # Get the index of the largest output value
+        max_index = np.argmax(predictions)
+        st.write(f"result (AutoModel) - Label: {max_index}")
+
+
+if __name__ == "__main__":
+    main()