Create app.py

app.py

@@ -0,0 +1,51 @@
+import streamlit as st
+from transformers import pipeline
+import streamlit as st
+import json
+import torch
+from torch.nn import functional as F
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+@st.cache_resource
+def load_dicts():
+    with open("label2ind.json", "r") as file:
+        label2ind = json.load(file)
+    with open("ind2label.json", "r") as file:
+        ind2label = json.load(file)
+    return label2ind, ind2label
+
+@st.cache_resource
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained("microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract")
+    model = AutoModelForSequenceClassification.from_pretrained(
+        "my_model/checkpoint-23000",
+        num_labels=len(label2ind),
+        problem_type="single_label_classification",
+    )
+    return tokenizer, model
+
+label2ind, ind2label = load_dicts()
+tokenizer, model = load_model()
+
+title = st.text_input("Title", value="Math")
+abstract = st.text_input("Abstract", value="Random variable")
+
+def get_logits(title, abstract):
+    text = title + "###" + abstract
+    logits = model(tokenizer(text, return_tensors="pt")['input_ids'])['logits']
+    return logits
+
+def get_ans(logits):
+    ind = torch.argsort(logits, dim=1, descending=True)
+    logits = F.softmax(logits)
+    cum_sum = 0
+    i = 0
+    while cum_sum < 0.95:
+        cum_sum += logits[0][ind[i]]
+        st.write(f"label {ind2label[ind[i]]} with probability {logits[0][ind[i]] * 100}%")
+        i +=1
+
+if title or abstract:
+    st.write(query)
+    st.write(result)
+    logits = get_logits(text, abstract)