add app.py

app.py

@@ -0,0 +1,49 @@
+from distutils.command.upload import upload
+from io import StringIO
+import pandas as pd
+import streamlit as st
+
+from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering
+
+
+@st.cache
+def load_data(file):
+    df = pd.read_csv(file, encoding='utf-8', nrows=50)
+    return df
+
+
+@st.cache
+def load_model_tokenizer():
+    tokenizer_cp = "albert-base-v2"
+    model_cp = "aidan-o-brien/recipe-improver"
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_cp)
+    model = TFAutoModelForQuestionAnswering.from_pretrained(model_cp)
+    return model, tokenizer
+
+
+# Page config
+title = "Recipe Improver"
+icon = "🍣"
+st.set_page_config(page_title=title, page_icon=icon)
+st.title(title)
+
+# Load csv
+uploaded_file = st.file_uploader("Choose a csv file", type="csv", key='file_uploader')
+if uploaded_file is not None:
+    df = load_data(uploaded_file)
+    st.write(df.head())
+
+# Load tokenizer and model
+model, tokenizer = load_model_tokenizer()
+st.write("Model and tokenizer successfully loaded.")
+
+# Pre-process data from csv file
+
+
+# Run inference
+
+
+# Post-process output of model
+
+
+# Present results