Create app.py

app.py

@@ -0,0 +1,62 @@
+# app.py
+import streamlit as st
+from transformers import pipeline, TFAutoModelForSequenceClassification, AutoTokenizer
+from datasets import load_dataset
+import pandas as pd
+
+st.set_page_config(layout="wide")
+
+# Load dataset
+@st.cache_resource
+def load_data():
+    dataset = load_dataset("WhiteAngelss/Turkce-Duygu-Analizi-Dataset")
+    return dataset
+
+dataset = load_data()
+st.title("Sentiment Analysis with Turkish Dataset")
+
+# Show dataset examples
+st.subheader("Sample Data")
+sample_df = pd.DataFrame(dataset['train'])
+st.write(sample_df.head())
+
+# Set up model
+model_list = ['WhiteAngelss/entity-word-sentiment-analysis']
+st.sidebar.header("Select Sentiment Analysis Model")
+model_checkpoint = st.sidebar.radio("", model_list)
+
+st.sidebar.write("For details of models: 'https://huggingface.co/WhiteAngelss/entity-word-sentiment-analysis'")
+st.sidebar.write("")
+
+st.subheader("Select Text Input Method")
+input_method = st.radio("", ('Select from Examples', 'Write or Paste New Text'))
+
+if input_method == 'Select from Examples':
+    example_texts = dataset['train']['text'][:5]  # Sample examples from the dataset
+    selected_text = st.selectbox('Select Text from List', example_texts)
+    st.subheader("Text to Analyze")
+    input_text = st.text_area("Selected Text", selected_text, height=128, max_chars=None)
+elif input_method == "Write or Paste New Text":
+    st.subheader("Text to Analyze")
+    input_text = st.text_area('Write or Paste Text Below', value="", height=128, max_chars=None)
+
+@st.cache_resource
+def setModel(model_checkpoint):
+    model = TFAutoModelForSequenceClassification.from_pretrained(model_checkpoint)
+    tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+    return pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
+
+Run_Button = st.button("Run", key=None)
+
+if Run_Button and input_text:
+    sentiment_pipeline = setModel(model_checkpoint)
+    output = sentiment_pipeline(input_text)
+    
+    st.subheader("Sentiment Analysis Results")
+    df = pd.DataFrame(output)
+    st.dataframe(df)
+
+    # Display the sentiment in a more user-friendly format
+    sentiment = output[0]['label']
+    score = output[0]['score']
+    st.write(f"Sentiment: {sentiment} (Score: {score:.2f})")