Added basic structure

app.py

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+import streamlit as st
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+
+st.title("Sentiment Analysis App - beta")
+st.header("This app is to analyze the sentiments behind a text. Currently it uses \
+          pre-trained models without fine-tuning.")
+
+st.text_input("Enter your text:", value="Missing Sophie.Z...")
+st.selectbox("Please select a model:" ("Model 1", "Model 2", "Model 3"))
+
+if st.button("Analyze"):
+    st.write("You clicked a button.")
+else:
+    st.write("Go on! Try the app!")

milestone3.py

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+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+
+import torch
+import torch.nn.functional as F
+
+model_name = "distilbert-base-uncased-finetuned-sst-2-english"
+
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+res = classifier(["I am very happy now.", "Not happy now."])
+
+for result in res:
+    print(result)
+
+tokens = tokenizer.tokenize("I am very happy now.")
+token_ids = tokenizer.convert_tokens_to_ids(tokens)
+input_ids = tokenizer("I am very happy now.")
+
+print(f'Tokens:{tokens}')
+print(f'TokenIDs:{token_ids}')
+print(f'InputIDs:{input_ids}')

requirements.txt

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+transformers