app.py

import streamlit as st
import requests
import pandas as pd
import io
import os
from PIL import Image
import time

# Configure page
st.set_page_config(
    page_title="PromptPrepML - Auto ML Data Preprocessing",
    page_icon="🤖",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Custom CSS for better styling
st.markdown("""
<style>
    .main-header {
        font-size: 2.5rem;
        font-weight: bold;
        color: #1f2937;
        text-align: center;
        margin-bottom: 2rem;
    }
    .step-header {
        font-size: 1.5rem;
        font-weight: 600;
        color: #374151;
        margin: 1rem 0;
    }
    .success-box {
        background-color: #f0fdf4;
        border: 1px solid #bbf7d0;
        border-radius: 0.5rem;
        padding: 1rem;
        margin: 1rem 0;
    }
    .info-box {
        background-color: #eff6ff;
        border: 1px solid #bfdbfe;
        border-radius: 0.5rem;
        padding: 1rem;
        margin: 1rem 0;
    }
    .warning-box {
        background-color: #fffbeb;
        border: 1px solid #fed7aa;
        border-radius: 0.5rem;
        padding: 1rem;
        margin: 1rem 0;
    }
</style>
""", unsafe_allow_html=True)

# API base URLs - try deployed backend first, fallback to localhost
DEPLOYED_BACKEND = "https://promptprepml-backend.railway.app"
LOCAL_BACKEND = "http://localhost:8000"

def check_backend_health():
    """Check if backend is running (try deployed first, then local)"""
    backends = [DEPLOYED_BACKEND, LOCAL_BACKEND]
    
    for backend_url in backends:
        try:
            response = requests.get(f"{backend_url}/health", timeout=5)
            if response.status_code == 200:
                st.session_state.backend_url = backend_url
                return True, backend_url
        except:
            continue
    return False, None

def upload_dataset(uploaded_file):
    """Upload dataset to backend"""
    if 'backend_url' not in st.session_state:
        return None, "Backend not connected"
    
    try:
        files = {'file': uploaded_file}
        response = requests.post(f"{st.session_state.backend_url}/upload-dataset", files=files)
        if response.status_code == 200:
            return response.json(), None
        else:
            return None, f"Upload failed: {response.text}"
    except Exception as e:
        return None, f"Upload error: {str(e)}"

def process_pipeline(uploaded_file, prompt):
    """Process dataset through ML pipeline"""
    if 'backend_url' not in st.session_state:
        return None, "Backend not connected"
    
    try:
        files = {'file': uploaded_file}
        data = {'prompt': prompt}
        response = requests.post(f"{st.session_state.backend_url}/process-pipeline", files=files, data=data)
        if response.status_code == 200:
            return response.json(), None
        else:
            return None, f"Processing failed: {response.text}"
    except Exception as e:
        return None, f"Processing error: {str(e)}"

def download_file(filename):
    """Download processed file"""
    if 'backend_url' not in st.session_state:
        return None, "Backend not connected"
    
    try:
        response = requests.get(f"{st.session_state.backend_url}/api/download/{filename}")
        if response.status_code == 200:
            return response.content, None
        else:
            return None, f"Download failed: {response.text}"
    except Exception as e:
        return None, f"Download error: {str(e)}"

def main():
    # Main header
    st.markdown('<h1 class="main-header">🤖 PromptPrepML</h1>', unsafe_allow_html=True)
    st.markdown('<p style="text-align: center; color: #6b7280; font-size: 1.1rem;">Convert natural language prompts into ML-ready datasets</p>', unsafe_allow_html=True)
    
    # Check backend health
    backend_healthy, backend_url = check_backend_health()
    
    if not backend_healthy:
        st.error("❌ Backend is not running! Please start the backend:")
        st.code("""
cd promptprepml/backend
venv\\Scripts\\activate
python app/main.py

# OR wait for deployed backend to be ready
""")
        st.info("🚀 **Deploying backend to cloud...** This will make the app work standalone!")
        return
    
    st.success(f"✅ Backend connected at: {backend_url}")
    
    # Sidebar for navigation
    st.sidebar.title("📋 Processing Steps")
    
    # Initialize session state
    if 'step' not in st.session_state:
        st.session_state.step = 'upload'
    if 'upload_result' not in st.session_state:
        st.session_state.upload_result = None
    if 'processing_result' not in st.session_state:
        st.session_state.processing_result = None
    
    # Step indicators
    steps = ['📤 Upload', '⚙️ Configure', '🚀 Process', '📊 Results']
    current_step_index = 0
    
    if st.session_state.step == 'upload':
        current_step_index = 0
    elif st.session_state.step == 'configure':
        current_step_index = 1
    elif st.session_state.step == 'process':
        current_step_index = 2
    elif st.session_state.step == 'results':
        current_step_index = 3
    
    # Display step indicators
    for i, step in enumerate(steps):
        if i < current_step_index:
            st.sidebar.success(f"✅ {step}")
        elif i == current_step_index:
            st.sidebar.info(f"🔄 {step}")
        else:
            st.sidebar.write(f"⏳ {step}")
    
    # Step 1: Upload Dataset
    if st.session_state.step == 'upload':
        st.markdown('<h2 class="step-header">📤 Step 1: Upload Dataset</h2>', unsafe_allow_html=True)
        
        uploaded_file = st.file_uploader(
            "Choose a CSV file",
            type=['csv'],
            help="Upload your dataset for preprocessing"
        )
        
        if uploaded_file is not None:
            st.info(f"📄 File uploaded: `{uploaded_file.name}`")
            
            # Show file preview
            try:
                df = pd.read_csv(uploaded_file)
                st.markdown('<div class="info-box">', unsafe_allow_html=True)
                st.markdown(f"**Dataset Shape:** {df.shape}")
                st.markdown(f"**Columns:** {', '.join(df.columns)}")
                st.dataframe(df.head())
                st.markdown('</div>', unsafe_allow_html=True)
                
                if st.button("🚀 Continue to Configuration", type="primary"):
                    # Upload to backend
                    with st.spinner("Uploading dataset..."):
                        result, error = upload_dataset(uploaded_file)
                        if error:
                            st.error(f"❌ Upload failed: {error}")
                        else:
                            st.session_state.upload_result = result
                            st.session_state.step = 'configure'
                            st.rerun()
            except Exception as e:
                st.error(f"❌ Error reading file: {str(e)}")
    
    # Step 2: Configure Processing
    elif st.session_state.step == 'configure':
        st.markdown('<h2 class="step-header">⚙️ Step 2: Configure Processing</h2>', unsafe_allow_html=True)
        
        if st.session_state.upload_result:
            file_info = st.session_state.upload_result
            st.markdown('<div class="info-box">', unsafe_allow_html=True)
            st.markdown(f"**File:** {file_info.get('filename', 'Unknown')}")
            st.markdown(f"**Size:** {file_info.get('size', 'Unknown')} bytes")
            st.markdown('</div>', unsafe_allow_html=True)
        
        # Processing options
        prompt = st.text_area(
            "Describe your preprocessing needs:",
            value="Prepare this dataset for machine learning. Handle missing values, remove identifier columns, extract date features, encode categorical variables, and scale numeric features.",
            height=100,
            help="Describe what you want to do with your dataset in natural language"
        )
        
        col1, col2 = st.columns([1, 1])
        with col1:
            if st.button("⬅️ Back", type="secondary"):
                st.session_state.step = 'upload'
                st.rerun()
        
        with col2:
            if st.button("🚀 Start Processing", type="primary"):
                if uploaded_file is not None:
                    with st.spinner("Processing dataset... This may take a few minutes."):
                        result, error = process_pipeline(uploaded_file, prompt)
                        if error:
                            st.error(f"❌ Processing failed: {error}")
                        else:
                            st.session_state.processing_result = result
                            st.session_state.step = 'results'
                            st.rerun()
    
    # Step 3: Results
    elif st.session_state.step == 'results':
        st.markdown('<h2 class="step-header">📊 Step 3: Results</h2>', unsafe_allow_html=True)
        
        if st.session_state.processing_result:
            result = st.session_state.processing_result
            
            # Success message
            st.markdown('<div class="success-box">', unsafe_allow_html=True)
            st.success("✅ Dataset processed successfully!")
            st.markdown('</div>', unsafe_allow_html=True)
            
            # Results summary
            col1, col2 = st.columns([2, 1])
            
            with col1:
                st.markdown("### 📈 Processing Summary")
                
                dataset_info = result.get('dataset_info', {})
                if dataset_info:
                    basic_info = dataset_info.get('basic_info', {})
                    st.markdown(f"- **Original Shape:** {basic_info.get('shape', 'Unknown')}")
                    st.markdown(f"- **Columns:** {basic_info.get('columns', 'Unknown')}")
                
                preprocessing_info = result.get('preprocessing_info', {})
                if preprocessing_info:
                    st.markdown(f"- **Processed Shape:** {preprocessing_info.get('processed_shape', 'Unknown')}")
                
                # Dataset preview
                st.markdown("### 👀 Dataset Preview")
                preview_data = result.get('preview_data', [])
                if preview_data:
                    df_preview = pd.DataFrame(preview_data)
                    st.dataframe(df_preview)
            
            with col2:
                st.markdown("### 📥 Download Files")
                
                download_links = [
                    ("Processed Dataset", "processed_dataset.csv"),
                    ("Training Set", "train.csv"),
                    ("Test Set", "test.csv"),
                    ("Pipeline", "pipeline.pkl"),
                    ("EDA Report", "eda_report.html")
                ]
                
                for name, filename in download_links:
                    if st.button(f"📥 {name}", key=f"download_{filename}"):
                        with st.spinner(f"Downloading {filename}..."):
                            file_content, error = download_file(filename)
                            if error:
                                st.error(f"❌ Download failed: {error}")
                            else:
                                st.download_button(
                                    label=f"💾 Save {filename}",
                                    data=file_content,
                                    file_name=filename,
                                    mime="application/octet-stream"
                                )
        
        # Action buttons
        col1, col2 = st.columns([1, 1])
        with col1:
            if st.button("🔄 Process New Dataset", type="secondary"):
                # Reset session state
                for key in list(st.session_state.keys()):
                    del st.session_state[key]
                st.session_state.step = 'upload'
                st.rerun()
        
        with col2:
            if st.button("📈 View EDA Report", type="primary"):
                st.info("📊 EDA Report feature coming soon!")
    
    # Footer
    st.markdown("---")
    st.markdown("""
    <div style="text-align: center; color: #6b7280; margin-top: 2rem;">
        <p><strong>PromptPrepML</strong> - Automated ML Data Preprocessing</p>
        <p><small>Convert natural language prompts into ML-ready datasets</small></p>
    </div>
    """, unsafe_allow_html=True)

if __name__ == "__main__":
    main()