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import streamlit as st
import pandas as pd
import joblib
# Load your saved model and vectorizer
model = joblib.load('naive_bayes_model.pkl')
vectorizer = joblib.load('count_vectorizer.pkl')
# Streamlit App Title
st.title("Text Classification App")
st.write("This app classifies text into 'Spam' or 'Not Spam' categories. You can input text or upload a CSV file for bulk classification.")
# Section: Single Text Input
st.header("Single Text Classification")
user_input = st.text_area("Enter your text here:")
if st.button("Classify"):
if user_input.strip():
# Preprocess the input
input_vector = vectorizer.transform([user_input])
prediction = model.predict(input_vector)
confidence = model.predict_proba(input_vector).max() * 100
# Display results
st.write(f"Prediction: {'Spam' if prediction[0] == 1 else 'Not Spam'}")
st.write(f"Confidence Score: {confidence:.2f}%")
else:
st.warning("Please enter some text for classification.")
# Section: Bulk File Classification
st.header("Bulk Classification from CSV")
uploaded_file = st.file_uploader("Upload a CSV file for bulk classification", type=["csv"])
if uploaded_file:
try:
# Attempt to read the file with the correct encoding
df = pd.read_csv(uploaded_file, encoding='latin1') # Use 'latin1' encoding for non-UTF-8 files
if 'Text' in df.columns:
input_vectors = vectorizer.transform(df['Text'])
df['Prediction'] = model.predict(input_vectors)
df['Confidence Score'] = model.predict_proba(input_vectors).max(axis=1) * 100
# Display first few predictions
st.write("First few predictions:")
st.write(df.head())
# Visualization: Prediction distribution
st.subheader("Prediction Distribution")
st.bar_chart(df['Prediction'].value_counts())
# Confidence threshold filter
st.subheader("Filter Results by Confidence Score")
threshold = st.slider("Confidence Threshold", 0, 100, 50)
filtered_df = df[df['Confidence Score'] >= threshold]
st.write("Filtered Results:")
st.write(filtered_df)
# Download filtered predictions
st.subheader("Download Filtered Predictions")
csv = filtered_df.to_csv(index=False)
st.download_button(
label="Download Filtered Predictions",
data=csv,
file_name="filtered_predictions.csv",
mime="text/csv"
)
else:
st.error("The uploaded file must contain a 'text' column.")
except Exception as e:
st.error(f"An error occurred while processing the file: {e}")
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