Update app.py

app.py

@@ -1,35 +1,91 @@
 import streamlit as st
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-
-# Tạo sidebar cho upload file
-st.sidebar.title("Upload Dataset")
-uploaded_file = st.sidebar.file_uploader("Chọn một file CSV", type=["csv"])
-
-# Kiểm tra xem đã upload file chưa
-if uploaded_file is not None:
-    # Đọc dữ liệu từ file CSV
-    df = pd.read_csv(uploaded_file)
-
-    # Hiển thị dữ liệu
-    st.subheader("Dữ liệu từ file CSV")
-    st.write(df)
-
-    # Thống kê số lượng nhãn
-    st.subheader("Thống kê số lượng nhãn")
-    label_counts = df['label_id'].value_counts()
-    
-    # Hiển thị số lượng free_text của mỗi nhãn
-    st.write("Số lượng nhãn OFFENSIVE (2):", label_counts.get(2, 0))
-    st.write("Số lượng nhãn Clean (0):", label_counts.get(0, 0))
-    st.write("Số lượng nhãn OFFENSIVE (1):", label_counts.get(1, 0))
-
-    # Hiển thị biểu đồ thống kê
-    st.subheader("Biểu đồ thống kê")
-    fig, ax = plt.subplots(figsize=(8, 5))
-    sns.countplot(x='label_id', data=df, ax=ax)
-    st.pyplot(fig)
-
-else:
-    st.warning("Vui lòng upload file CSV.")
+from pyspark.sql import SparkSession
+from pyspark.ml.pipeline import Pipeline, PipelineModel
+from pyspark.sql.types import *
+from pyspark.sql.functions import *
+
+from pyspark.sql import DataFrame
+from pyspark import keyword_only
+from pyspark.ml import Transformer
+from pyspark.ml.param.shared import HasInputCol, HasOutputCol, Param, Params, TypeConverters
+from pyspark.ml.util import DefaultParamsReadable, DefaultParamsWritable
+
+import re
+import string
+
+# Load Spark session
+spark = SparkSession.builder\
+    .appName("HateSpeechDetection")\
+    .master('local[*]')\
+    .getOrCreate()
+
+# Load the pre-trained model
+loaded_model = PipelineModel.load('LogisticRegression')
+
+# Define the TextTransformer class (as in your code)
+class TextTransformer(Transformer, HasInputCol, HasOutputCol, DefaultParamsReadable, DefaultParamsWritable):
+    input_col = Param(Params._dummy(), "input_col", "input column name.", typeConverter=TypeConverters.toString)
+    output_col = Param(Params._dummy(), "output_col", "output column name.", typeConverter=TypeConverters.toString)
+
+    @keyword_only
+    def __init__(self, input_col: str = "input", output_col: str = "output", ):
+        super(TextTransformer, self).__init__()
+        self._setDefault(input_col=None, output_col=None)
+        kwargs = self._input_kwargs
+        self.set_params(**kwargs)
+
+
+    @keyword_only
+    def set_params(self, input_col: str = "input", output_col: str = "output"):
+        kwargs = self._input_kwargs
+        self._set(**kwargs)
+
+    def get_input_col(self):
+        return self.getOrDefault(self.input_col)
+
+    def get_output_col(self):
+        return self.getOrDefault(self.output_col)
+
+
+    def _transform(self, df: DataFrame):
+        def preprocess_text(text, ) -> str:
+            text = re.sub(r'\d+', '', str(text)).translate(str.maketrans( string.punctuation, ' '*len(string.punctuation)),).strip().lower()
+            return text
+        input_col = self.get_input_col()
+        output_col = self.get_output_col()
+        # The custom action: concatenate the integer form of the doubles from the Vector
+        transform_udf = udf(preprocess_text, StringType())
+        new_df = df.withColumn(output_col, transform_udf(input_col))
+        return new_df
+
+# Create a Streamlit app
+def main():
+    st.title("Text Classification App")
+
+    # User input text
+    user_input = st.text_area("Enter text here:")
+
+    if st.button("Predict"):
+        if user_input:
+            # Create a DataFrame with a single column 'free_text' containing the input text
+            data = [(user_input,)]
+            columns = ['free_text']
+            input_df = spark.createDataFrame(data, columns)
+
+            # Use the loaded model to make predictions
+            predictions = loaded_model.transform(input_df)
+
+            # Extract the prediction result
+            result = predictions.select("prediction").collect()[0]["prediction"]
+
+            # Map the prediction result to corresponding labels
+            labels = {0: "CLEAN", 1: "OFFENSIVE", 2: "HATE"}
+            predicted_class = labels.get(result, "UNKNOWN")
+
+            # Display the result
+            st.success(f"Predicted class: {predicted_class}")
+        else:
+            st.warning("Please enter some text.")
+
+if __name__ == "__main__":
+    main()