Create app.py

app.py

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+
+import streamlit as st 
+from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
+from transformers import pipeline
+
+
+def multilingualmodel():
+    st.markdown("# multilingual model 🎈")
+    st.sidebar.markdown("# nlptown/bert-base-multilingual-uncased-sentiment🎈")
+    st.write("This classifier can now deal with texts in English, French, but also Dutch, German, Italian and Spanish!")
+    classifier = pipeline('sentiment-analysis')
+    model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
+    model = TFAutoModelForSequenceClassification.from_pretrained(model_name, from_pt=True)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
+    user_input = st.text_area('Enter Text to Analyze')
+    button = st.button("Analyze")
+    if user_input and button :
+      tt = classifier(user_input)
+      st.write(tt)
+      for result in tt:
+        st.success(f"label: {result['label']}, with score: {round(result['score'], 4)}")
+
+
+def engdistilbertmod():
+    st.markdown("distilbert base finetuned english ❄️")
+    st.sidebar.markdown("# distilbert-base-uncased-finetuned-sst-2-english ❄️")
+    model_name = "distilbert-base-uncased-finetuned-sst-2-english"
+    tf_model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    classifier = pipeline('sentiment-analysis', model=tf_model, tokenizer=tokenizer)
+
+    user_input = st.text_area('Enter Text to Analyze With distilbert ', key= "distilbert_input")
+    button = st.button("Analyze", key= "distilbert_button")
+
+    if user_input and button :
+        tt = classifier(user_input)
+        for result in tt:
+            st.success(f"label: {result['label']}, with score: {round(result['score'], 4)}")
+
+
+page_names_to_funcs = {
+    "Bert-base-Multilingual": multilingualmodel,
+    "Distilbert base": engdistilbertmod,
+}
+
+selected_page = st.sidebar.selectbox("Select a page", page_names_to_funcs.keys())
+page_names_to_funcs[selected_page]()
+