Added app files

app.py

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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}")

count_vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c0a3641d27bf9a3dc8ac9ca3cea1a7019f934c47f9dbf0717ae1772280a4b3e1
+size 84564

naive_bayes_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:78c7ff428289a9c05b5bdbcec751090bd51b85bd6c93bc6bb6f7eed3fe7137df
+size 223063

requirements.txt

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+streamlit
+scikit-learn
+joblib
+pandas
+numpy

vscode.txt

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+pip install pandas
+