Deploy Transformers on AWS Fargate

streamlit_fargat/Dockerfile

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+FROM python:3.9.10
+
+WORKDIR /app
+
+COPY ./requirements.txt requirements.txt
+COPY ./app.py app.py
+
+RUN pip install -r requirements.txt 
+
+EXPOSE 8501
+
+CMD streamlit run app.py

streamlit_fargat/app.py

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+import streamlit as st
+import numpy as np
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+
+@st.cache(allow_output_mutation=True)
+def get_model():
+    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+    model = BertForSequenceClassification.from_pretrained("pnichite/YTFineTuneBert")
+    return tokenizer,model
+
+
+tokenizer,model = get_model()
+
+user_input = st.text_area('Enter Text to Analyze')
+button = st.button("Analyze")
+
+d = {
+
+  1:'Toxic',
+  0:'Non Toxic'
+}
+
+if user_input and button :
+    test_sample = tokenizer([user_input], padding=True, truncation=True, max_length=512,return_tensors='pt')
+    # test_sample
+    output = model(**test_sample)
+    # st.write("Logits: ",output.logits)
+    y_pred = np.argmax(output.logits.detach().numpy(),axis=1)
+    st.write("Prediction: ",d[y_pred[0]])

streamlit_fargat/requirements.txt

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+streamlit
+torch
+transformers