Add streamlit app

.gitignore

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+# Venv
+.env

app.py

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+from transformers import AutoModel, AutoTokenizer
+import streamlit as st
+
+
+st.header("MeshTagger 🔖")
+threshold = st.sidebar.slider("Threshold", value=0.5, min_value=0.0, max_value=1.0)
+display_probabilities = st.sidebar.checkbox("Display probabilities")
+
+if "model" not in st.session_state:
+    with st.spinner("Loading model and tokenizer..."):
+        st.session_state["tokenizer"] = AutoTokenizer.from_pretrained(
+            "Wellcome/WellcomeBertMesh"
+        )
+        st.session_state["model"] = AutoModel.from_pretrained(
+            "Wellcome/WellcomeBertMesh", trust_remote_code=True
+        )
+
+model = st.session_state["model"]
+tokenizer = st.session_state["tokenizer"]
+
+text = st.text_area("", value="This text is about Malaria", height=400)
+inputs = tokenizer([text], padding="max_length")
+outputs = model(**inputs)[0]
+
+if display_probabilities:
+    data = [
+        (model.id2label[label_id], label_prob.item())
+        for label_id, label_prob in enumerate(outputs)
+        if label_prob > threshold
+    ]
+    st.table(data)
+else:
+    for label_id, label_prob in enumerate(outputs):
+        if label_prob > threshold:
+            st.button(model.id2label[label_id])