Create test-app.py

test-app.py

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+import streamlit as st
+import requests
+from transformers import pipeline
+
+############ 1. PAGE LAYOUT, TITLE
+st.set_page_config(
+    layout="centered", page_title='Simple Sentiment Analysis App Using\n\
+        Hugging Face Model Library', page_icon="(y)"
+)
+
+c1,c2,c3 = st.columns([1,3,1])
+
+with c1:
+    st.write("")
+with c2:
+    st.image("images/emotions.png")
+with c3:
+    st.write("")
+
+# prepare a list of top sentiment analysis models including default
+models = ["distilbert-base-uncased-finetuned-sst-2-english",#default
+          "bhadresh-savani/distilbert-base-uncased-emotion",#emotions
+          "ProsusAI/finbert",#finance
+          "finiteautomata/bertweet-base-sentiment-analysis",#tweets
+          "cardiffnlp/twitter-roberta-base-sentiment"#tweet2
+]
+model_pointers = ["default: distilbert-base-uncased-finetuned-sst-2-english",
+                  "emotion: bhadresh-savani/distilbert-base-uncased-emotion",
+                  "finance: ProsusAI/finbert",
+                  "tweets: finiteautomata/bertweet-base-sentiment-analysis",
+                  "tweets2: cardiffnlp/twitter-roberta-base-sentiment"
+]
+
+#Prompt User for input text for sentiment analysis, keep input and model selection in form to delay page refresh
+with st.form(key="init_form"):
+    input_text = st.text_area("Input a sentence on which to perform sentiment\
+                           analysis", value="I love Streamlit and I love Data Science!")
+    choice = st.selectbox("Choose Model", model_pointers)
+
+    # The index of choice in model_pointers will access the models list
+    # and select the Hugging Face model path at index.  
+    user_picked_model = models[model_pointers.index(choice)]
+    with st.spinner("Downloading Model"):
+        sentiment_pipeline = pipeline(model=user_picked_model)
+
+    analyze = st.form_submit_button("Analyze")
+
+if analyze:
+    with st.spinner("Analyzing..."):
+        sentiment_pipeline = pipeline(model=user_picked_model)
+        sentiment_results=sentiment_pipeline(input_text)
+        st.write(f"Sentiment: {sentiment_results[0]['label']}")
+        st.write(f"Score: {sentiment_results[0]['score']}")
+else:
+    st.write("no input detected")
+