app.py

import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import os
import pickle
import re
import string
from pathlib import Path

# Configure Streamlit page
st.set_page_config(page_title="No Code Text Classifier", page_icon="🤖", layout="wide")

# Lazy imports to speed up startup
@st.cache_resource
def load_ml_libraries():
    """Lazy load ML libraries only when needed"""
    try:
        from sklearn.preprocessing import LabelEncoder
        from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
        from sklearn.model_selection import train_test_split
        from sklearn.metrics import accuracy_score
        from sklearn.linear_model import LogisticRegression
        from sklearn.tree import DecisionTreeClassifier
        from sklearn.svm import LinearSVC
        from sklearn.ensemble import RandomForestClassifier
        from sklearn.naive_bayes import MultinomialNB
        
        return {
            'LabelEncoder': LabelEncoder,
            'TfidfVectorizer': TfidfVectorizer,
            'CountVectorizer': CountVectorizer,
            'train_test_split': train_test_split,
            'accuracy_score': accuracy_score,
            'models': {
                "Logistic Regression": LogisticRegression,
                "Decision Tree": DecisionTreeClassifier,
                "Random Forest": RandomForestClassifier,
                "Linear SVC": LinearSVC,
                "Multinomial Naive Bayes": MultinomialNB,
            }
        }
    except ImportError as e:
        st.error(f"Error importing ML libraries: {e}")
        return None

# Basic stopwords (no NLTK dependency)
BASIC_STOPWORDS = {
    'i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', 
    'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 
    'himself', 'she', 'her', 'hers', 'herself', 'it', 'its', 'itself',
    'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which',
    'who', 'whom', 'this', 'that', 'these', 'those', 'am', 'is', 'are',
    'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having',
    'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if',
    'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for',
    'with', 'through', 'during', 'before', 'after', 'above', 'below',
    'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again',
    'further', 'then', 'once'
}

class TextCleaner:
    """Lightweight text cleaner without NLTK dependencies"""
    def __init__(self):
        self.currency_symbols = r'[\$\£\€\¥\₹\¢\₽\₩\₪]'
        self.stop_words = BASIC_STOPWORDS

    def remove_punctuation(self, text):
        return text.translate(str.maketrans('', '', string.punctuation))
    
    def clean_text(self, text):
        """Clean text with basic processing"""
        if not isinstance(text, str):
            text = str(text) if text is not None else ""
        
        if not text.strip():
            return ""
        
        try:
            # Basic cleaning
            text = text.lower()
            text = re.sub(self.currency_symbols, 'currency', text)
            
            # Remove emojis (simplified pattern)
            text = re.sub(r'[^\w\s]', ' ', text)
            text = re.sub(r'\d+', ' ', text)
            text = re.sub(r'\s+', ' ', text).strip()
            
            # Remove stopwords
            words = [word for word in text.split() if word not in self.stop_words and len(word) > 2]
            
            return ' '.join(words)
            
        except Exception as e:
            st.warning(f"Text cleaning warning: {e}")
            return str(text).lower()

class DataAnalyzer:
    """Lightweight data analyzer"""
    def __init__(self, df, text_column, target_column):
        self.df = df
        self.text_column = text_column
        self.target_column = target_column
    
    def get_basic_info(self):
        info = {
            'shape': self.df.shape,
            'missing_values': self.df.isnull().sum().to_dict(),
            'class_distribution': self.df[self.target_column].value_counts().to_dict()
        }
        return info
    
    def plot_class_distribution(self):
        try:
            fig, ax = plt.subplots(figsize=(8, 5))
            self.df[self.target_column].value_counts().plot(kind='bar', ax=ax, color='steelblue')
            ax.set_title('Class Distribution')
            ax.set_xlabel('Classes')
            ax.set_ylabel('Count')
            plt.xticks(rotation=45)
            plt.tight_layout()
            st.pyplot(fig)
            plt.close()
        except Exception as e:
            st.error(f"Error creating plot: {e}")
    
    def plot_text_length_distribution(self):
        try:
            fig, ax = plt.subplots(figsize=(8, 5))
            text_lengths = self.df[self.text_column].astype(str).str.len()
            ax.hist(text_lengths, bins=30, alpha=0.7, color='lightcoral')
            ax.set_title('Text Length Distribution')
            ax.set_xlabel('Text Length (characters)')
            ax.set_ylabel('Frequency')
            plt.tight_layout()
            st.pyplot(fig)
            plt.close()
        except Exception as e:
            st.error(f"Error creating plot: {e}")

# Utility functions
def save_artifacts(obj, folder_name, file_name):
    """Save artifacts with error handling"""
    try:
        os.makedirs(folder_name, exist_ok=True)
        file_path = os.path.join(folder_name, file_name)
        with open(file_path, 'wb') as f:
            pickle.dump(obj, f)
        return True
    except Exception as e:
        st.error(f"Error saving {file_name}: {e}")
        return False

def load_artifacts(folder_name, file_name):
    """Load artifacts with error handling"""
    try:
        file_path = os.path.join(folder_name, file_name)
        with open(file_path, 'rb') as f:
            return pickle.load(f)
    except FileNotFoundError:
        st.error(f"File {file_name} not found in {folder_name}")
        return None
    except Exception as e:
        st.error(f"Error loading {file_name}: {e}")
        return None

def train_model(model_name, X_train, X_test, y_train, y_test, ml_libs):
    """Train model with optimized parameters"""
    try:
        os.makedirs("models", exist_ok=True)
        
        # Get model class
        model_class = ml_libs['models'].get(model_name)
        if not model_class:
            st.error(f"Model {model_name} not supported")
            return None
        
        # Initialize model with faster parameters
        if model_name == "Logistic Regression":
            model = model_class(max_iter=500, random_state=42, solver='liblinear')
        elif model_name == "Random Forest":
            model = model_class(n_estimators=20, random_state=42, n_jobs=1)  # Reduced trees
        elif model_name == "Linear SVC":
            model = model_class(random_state=42, max_iter=500)
        else:
            model = model_class(random_state=42) if 'random_state' in model_class().get_params() else model_class()
        
        # Train model
        with st.spinner(f"Training {model_name}..."):
            model.fit(X_train, y_train)
        
        # Save model
        model_filename = f"{model_name.replace(' ', '_')}.pkl"
        
        if save_artifacts(model, "models", model_filename):
            # Quick evaluation
            y_pred = model.predict(X_test)
            accuracy = ml_libs['accuracy_score'](y_test, y_pred)
            
            st.success("✅ Model training completed!")
            st.write(f"**Accuracy**: {accuracy:.4f}")
            
            return model_filename
        else:
            return None
            
    except Exception as e:
        st.error(f"Error training model: {e}")
        return None

def predict_text(model_name, text, vectorizer_type="tfidf", ml_libs=None):
    """Make prediction with error handling"""
    try:
        # Load components
        model = load_artifacts("models", model_name)
        if model is None:
            return None, None
        
        vectorizer_file = f"{vectorizer_type}_vectorizer.pkl"
        vectorizer = load_artifacts("artifacts", vectorizer_file)
        if vectorizer is None:
            return None, None
        
        encoder = load_artifacts("artifacts", "encoder.pkl")
        if encoder is None:
            return None, None
        
        # Process text
        text_cleaner = TextCleaner()
        clean_text = text_cleaner.clean_text(text)
        
        if not clean_text.strip():
            st.warning("Text became empty after cleaning")
            return None, None
        
        # Vectorize and predict
        text_vector = vectorizer.transform([clean_text])
        prediction = model.predict(text_vector)
        
        # Get probabilities if available
        prediction_proba = None
        if hasattr(model, 'predict_proba'):
            try:
                prediction_proba = model.predict_proba(text_vector)[0]
            except:
                pass
        
        # Decode prediction
        predicted_label = encoder.inverse_transform(prediction)[0]
        
        return predicted_label, prediction_proba
        
    except Exception as e:
        st.error(f"Prediction error: {e}")
        return None, None

# Main Streamlit App
def main():
    st.title('🤖 No Code Text Classification App')
    st.write('Build and deploy text classification models without coding!')

    # Sidebar
    section = st.sidebar.radio("Choose Section", ["📊 Data Analysis", "🚀 Train Model", "🔮 Predictions"])

    # Upload Data
    st.sidebar.subheader("📁 Upload Your Dataset")
    train_data = st.sidebar.file_uploader("Upload training data (CSV)", type=["csv"])

    # Initialize session state
    if 'vectorizer_type' not in st.session_state:
        st.session_state.vectorizer_type = "tfidf"

    # Load and process data
    train_df = None
    if train_data is not None:
        try:
            # Try different encodings
            for encoding in ['utf-8', 'latin1', 'iso-8859-1', 'cp1252']:
                try:
                    train_df = pd.read_csv(train_data, encoding=encoding)
                    st.success(f"✅ File loaded successfully with {encoding} encoding")
                    break
                except UnicodeDecodeError:
                    continue
            
            if train_df is None:
                st.error("❌ Could not read the CSV file. Please check the file format.")
            else:
                st.write("**Training Data Preview:**")
                st.dataframe(train_df.head(3))
                
                columns = train_df.columns.tolist()
                text_data = st.sidebar.selectbox("📝 Choose the text column:", columns)
                target = st.sidebar.selectbox("🎯 Choose the target column:", columns)

                # Process data
                if text_data and target and st.sidebar.button("Process Data"):
                    with st.spinner("Processing data..."):
                        text_cleaner = TextCleaner()
                        
                        # Clean text with progress
                        progress_bar = st.progress(0)
                        cleaned_texts = []
                        for i, text in enumerate(train_df[text_data]):
                            cleaned_texts.append(text_cleaner.clean_text(text) if pd.notna(text) else "")
                            progress_bar.progress((i + 1) / len(train_df))
                        
                        train_df['clean_text'] = cleaned_texts
                        train_df['text_length'] = train_df[text_data].astype(str).str.len()
                        
                        # Handle label encoding
                        ml_libs = load_ml_libraries()
                        if ml_libs:
                            label_encoder = ml_libs['LabelEncoder']()
                            train_df['target'] = label_encoder.fit_transform(train_df[target].astype(str))
                            
                            # Save encoder
                            save_artifacts(label_encoder, "artifacts", "encoder.pkl")
                            st.success("✅ Data processed successfully!")
                        else:
                            st.error("❌ Could not load ML libraries")
                
        except Exception as e:
            st.error(f"❌ Error processing data: {e}")
            train_df = None

    # Data Analysis Section
    if section == "📊 Data Analysis":
        if train_df is not None and 'clean_text' in train_df.columns:
            st.subheader("📊 Data Insights")
            
            analyzer = DataAnalyzer(train_df, text_data, target)
            info = analyzer.get_basic_info()
            
            # Metrics
            col1, col2, col3 = st.columns(3)
            with col1:
                st.metric("📄 Total Samples", f"{info['shape'][0]:,}")
            with col2:
                st.metric("📊 Features", info['shape'][1])
            with col3:
                st.metric("🏷️ Classes", len(info['class_distribution']))
            
            # Class distribution
            st.write("**Class Distribution:**")
            class_dist_df = pd.DataFrame(list(info['class_distribution'].items()), 
                                       columns=['Class', 'Count'])
            st.dataframe(class_dist_df, use_container_width=True)
            
            # Sample data
            st.write("**Processed Data Sample:**")
            if 'clean_text' in train_df.columns:
                sample_df = train_df[['clean_text', 'text_length', target]].head(5)
                st.dataframe(sample_df, use_container_width=True)
            
            # Visualizations
            st.subheader("📈 Data Visualizations")
            
            col1, col2 = st.columns(2)
            with col1:
                st.write("**Class Distribution**")
                analyzer.plot_class_distribution()
            
            with col2:
                st.write("**Text Length Distribution**")
                analyzer.plot_text_length_distribution()
                
        else:
            st.info("📋 Upload and process your data to see analysis")

    # Train Model Section
    elif section == "🚀 Train Model":
        if train_df is not None and 'clean_text' in train_df.columns:
            st.subheader("🚀 Train Your Classification Model")

            col1, col2 = st.columns(2)

            with col1:
                model = st.selectbox("🤖 Choose Model", [
                    "Logistic Regression", 
                    "Decision Tree", 
                    "Random Forest", 
                    "Linear SVC",
                    "Multinomial Naive Bayes"
                ])
            
            with col2:
                vectorizer_choice = st.selectbox("📊 Choose Vectorizer", 
                                               ["Tfidf Vectorizer", "Count Vectorizer"])

            # Filter out empty texts
            valid_data = train_df[train_df['clean_text'].str.len() > 0].copy()
            
            if len(valid_data) < 10:
                st.error("❌ Not enough valid text data after cleaning! Need at least 10 samples.")
            else:
                st.info(f"✅ Ready to train with {len(valid_data):,} valid samples")
                
                # Load ML libraries when needed
                ml_libs = load_ml_libraries()
                if not ml_libs:
                    st.error("❌ Could not load ML libraries")
                    return

                # Initialize vectorizer
                max_features = min(5000, len(valid_data) * 5)  # Conservative limit
                
                if vectorizer_choice == "Tfidf Vectorizer":
                    vectorizer = ml_libs['TfidfVectorizer'](max_features=max_features, stop_words='english', ngram_range=(1,1))
                    st.session_state.vectorizer_type = "tfidf"
                else:
                    vectorizer = ml_libs['CountVectorizer'](max_features=max_features, stop_words='english', ngram_range=(1,1))
                    st.session_state.vectorizer_type = "count"

                if st.button("🎯 Start Training", type="primary"):
                    try:
                        # Vectorize
                        with st.spinner("Vectorizing text data..."):
                            X = vectorizer.fit_transform(valid_data['clean_text'])
                            y = valid_data['target']
                        
                        st.write(f"📊 **Feature matrix shape:** {X.shape}")
                        
                        # Split data
                        test_size = min(0.3, max(0.1, 50 / len(valid_data)))
                        X_train, X_test, y_train, y_test = ml_libs['train_test_split'](
                            X, y, test_size=test_size, random_state=42, stratify=y
                        )
                        
                        st.write(f"📈 **Data split** - Train: {X_train.shape[0]:,}, Test: {X_test.shape[0]:,}")
                        
                        # Save vectorizer
                        vectorizer_filename = f"{st.session_state.vectorizer_type}_vectorizer.pkl"
                        if save_artifacts(vectorizer, "artifacts", vectorizer_filename):
                            # Train model
                            model_filename = train_model(model, X_train, X_test, y_train, y_test, ml_libs)
                            if model_filename:
                                st.balloons()
                                st.success("🎉 Model ready! Go to 'Predictions' to test it.")
                    
                    except Exception as e:
                        st.error(f"❌ Training failed: {e}")
        else:
            st.info("📋 Please upload and process training data first")

    # Predictions Section
    elif section == "🔮 Predictions":
        st.subheader("🔮 Make Predictions")
        
        if os.path.exists("models") and os.listdir("models"):
            available_models = [f for f in os.listdir("models") if f.endswith('.pkl')]
            
            if available_models:
                selected_model = st.selectbox("🤖 Choose trained model:", available_models)
                
                text_input = st.text_area("📝 Enter text to classify:", 
                                        height=120, 
                                        placeholder="Type your text here...")
                
                col1, col2 = st.columns([1, 3])
                with col1:
                    predict_button = st.button("🎯 Predict", type="primary")
                
                if predict_button and text_input.strip():
                    ml_libs = load_ml_libraries()
                    if ml_libs:
                        predicted_label, prediction_proba = predict_text(
                            selected_model, 
                            text_input, 
                            st.session_state.get('vectorizer_type', 'tfidf'),
                            ml_libs
                        )
                        
                        if predicted_label is not None:
                            st.success("✅ Prediction completed!")
                            
                            # Show prediction
                            st.markdown("### 🏷️ Prediction Result")
                            st.markdown(f"**Predicted Class:** `{predicted_label}`")
                            
                            # Show probabilities if available
                            if prediction_proba is not None:
                                st.markdown("### 📊 Class Probabilities")
                                encoder = load_artifacts("artifacts", "encoder.pkl")
                                if encoder is not None:
                                    classes = encoder.classes_
                                    prob_df = pd.DataFrame({
                                        'Class': classes,
                                        'Probability': prediction_proba
                                    }).sort_values('Probability', ascending=False)
                                    
                                    # Create a nice probability display
                                    for idx, row in prob_df.iterrows():
                                        confidence = row['Probability']
                                        st.write(f"**{row['Class']}**: {confidence:.1%}")
                                        st.progress(confidence)
                elif predict_button:
                    st.warning("⚠️ Please enter some text to classify")
            else:
                st.info("📋 No trained models found")
        else:
            st.info("📋 No models available. Please train a model first in the 'Train Model' section.")

    # Footer
    st.markdown("---")
    st.markdown("🚀 **Built with Streamlit** | Ready for deployment on Hugging Face Spaces")

if __name__ == "__main__":
    main()