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app.py

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+import os
+os.system("pip install torch")
+os.system("pip install transformers")
+os.system("pip install sentencepiece")
+
+import streamlit as st
+from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("azizbarank/distilbert-base-turkish-cased-sentiment")
+model = AutoModelForSequenceClassification.from_pretrained("azizbarank/distilbert-base-turkish-cased-sentiment")
+
+def classify(text):
+    cls= pipeline("text-classification",model=model, tokenizer=tokenizer)
+    return cls(text)[0]['label']
+    
+
+site_header = st.container()
+text_input = st.container()
+model_results = st.container()
+
+with site_header:
+    st.title('Turkish Sentiment Analysis 😀😠')
+    st.markdown(
+    """
+    [Distilled Turkish BERT model](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) that I fine-tuned on the [sepidmnorozy/Turkish_sentiment](https://huggingface.co/datasets/sepidmnorozy/Turkish_sentiment) dataset that is heavily based on different reviews about services/places.
+    
+    For more information on the dataset:
+    
+    * [Hugging Face](https://huggingface.co/datasets/sepidmnorozy/Turkish_sentiment)
+    """
+)
+
+with text_input:
+    st.header('Is Your Review Considered Positive or Negative?')
+    st.write("""*Please note that predictions are based on how the model was trained, so it may not be an accurate representation.*""")
+    user_text = st.text_input('Enter Text', max_chars=300)
+
+with model_results:    
+    st.subheader('Prediction:')
+    if user_text:
+        prediction = classify(user_text)
+    
+        if prediction == "LABEL_0":
+            st.subheader('**Negative**')
+        else:
+            st.subheader('**Positive**')
+        st.text('')