utils/ui_components.py

import streamlit as st
from typing import Dict, List
from utils.recommendations import calculate_total_impact

def display_results(analysis_results: Dict, product_name: str):
    """Display analysis results in a formatted way"""
    
    col1, col2, col3 = st.columns(3)
    
    with col1:
        # Overall score
        score = analysis_results.get('overall_score', 5)
        if score >= 8:
            score_color = "🟢"
            score_text = "Excellent"
        elif score >= 6:
            score_color = "🟡"
            score_text = "Good"
        else:
            score_color = "🔴"
            score_text = "Needs Improvement"
        
        st.metric(
            label="Overall Score",
            value=f"{score}/10",
            delta=score_text,
            help="Overall assessment of product placement"
        )
    
    with col2:
        # Confidence
        confidence = analysis_results.get('confidence', 0.5)
        confidence_pct = int(confidence * 100)
        st.metric(
            label="AI Confidence",
            value=f"{confidence_pct}%",
            help="How confident the AI is in this analysis"
        )
    
    with col3:
        # Product found status
        found = analysis_results.get('product_found', False)
        status = "✅ Found" if found else "❌ Not Found"
        st.metric(
            label="Product Status",
            value=status,
            help="Whether the product was detected on the shelf"
        )
    
    st.markdown("---")
    
    # Detailed information
    col1, col2 = st.columns(2)
    
    with col1:
        st.markdown("### 📊 Product Details")
        
        # Facing count
        facing_count = analysis_results.get('facing_count', 0)
        st.info(f"**Facings:** {facing_count} visible units")
        
        # Shelf position
        position = analysis_results.get('shelf_position', 'Unknown')
        position_emoji = {
            'top': '⬆️',
            'middle': '➡️', 
            'bottom': '⬇️',
            'górna': '⬆️',
            'środkowa': '➡️',
            'dolna': '⬇️'
        }.get(position.lower(), '❓')
        st.info(f"**Position:** {position_emoji} {position.title()}")
        
        # Price visibility
        price_visible = analysis_results.get('price_visible', False)
        price_status = "✅ Visible" if price_visible else "❌ Not visible"
        st.info(f"**Price:** {price_status}")
    
    with col2:
        st.markdown("### 🔍 Quality Assessment")
        
        # Product condition
        condition = analysis_results.get('product_condition', 'Unknown')
        condition_emoji = {
            'good': '✅',
            'dusty': '🧹',
            'damaged': '⚠️',
            'dobry': '✅',
            'zakurzony': '🧹',
            'uszkodzony': '⚠️'
        }.get(condition.lower(), '❓')
        st.info(f"**Condition:** {condition_emoji} {condition.title()}")
        
        # Shelf share
        shelf_share = analysis_results.get('shelf_share', 0)
        if shelf_share > 0:
            st.info(f"**Shelf Share:** {shelf_share}% of shelf space")
        
        # Competitors
        competitors = analysis_results.get('competitors_nearby', [])
        if competitors:
            st.info(f"**Nearby Competitors:** {len(competitors)} products")
    
    # Description
    description = analysis_results.get('description', '')
    if description:
        st.markdown("### 📝 Analysis Details")
        st.markdown(f"> {description}")

def display_recommendations(recommendations: List[Dict]):
    """Display recommendations in a formatted way"""
    
    if not recommendations:
        st.info("No specific recommendations generated.")
        return
    
    # Calculate total impact
    impact_summary = calculate_total_impact(recommendations)
    
    # Display summary metrics
    col1, col2, col3, col4 = st.columns(4)
    
    with col1:
        st.metric(
            "Total Recommendations",
            impact_summary['total_recommendations'],
            help="Number of actionable recommendations"
        )
    
    with col2:
        st.metric(
            "High Priority",
            impact_summary['high_priority_count'],
            help="Critical issues requiring immediate attention"
        )
    
    with col3:
        st.metric(
            "Estimated Time",
            f"{impact_summary['total_time_minutes']} min",
            help="Total time needed to implement all changes"
        )
    
    with col4:
        # Calculate difficulty distribution
        difficulties = [r.get('difficulty', 'Unknown') for r in recommendations]
        easy_count = difficulties.count('Bardzo łatwe') + difficulties.count('Łatwe')
        difficulty_text = f"{easy_count}/{len(recommendations)} Easy"
        st.metric(
            "Difficulty",
            difficulty_text,
            help="How many recommendations are easy to implement"
        )
    
    st.markdown("---")
    
    # Group recommendations by priority
    high_priority = [r for r in recommendations if r.get('priority', 5) <= 2]
    medium_priority = [r for r in recommendations if r.get('priority', 5) == 3]
    low_priority = [r for r in recommendations if r.get('priority', 5) >= 4]
    
    # Display high priority recommendations
    if high_priority:
        st.markdown("### 🚨 High Priority (Do First)")
        for rec in high_priority:
            display_recommendation_card(rec)
    
    # Display medium priority recommendations
    if medium_priority:
        st.markdown("### ⚡ Medium Priority")
        for rec in medium_priority:
            display_recommendation_card(rec)
    
    # Display low priority recommendations  
    if low_priority:
        st.markdown("### 💡 Additional Improvements")
        for rec in low_priority:
            display_recommendation_card(rec)

def display_recommendation_card(recommendation: Dict):
    """Display a single recommendation as a card"""
    
    # Priority styling
    priority = recommendation.get('priority', 5)
    if priority <= 2:
        border_color = "#ff4b4b"  # Red
    elif priority == 3:
        border_color = "#ffa500"  # Orange
    else:
        border_color = "#1f77b4"  # Blue
    
    # Card styling
    st.markdown(f"""
    <div style="
        border: 2px solid {border_color};
        border-radius: 10px;
        padding: 15px;
        margin: 10px 0;
        background-color: white;
    ">
    </div>
    """, unsafe_allow_html=True)
    
    with st.container():
        col1, col2 = st.columns([3, 1])
        
        with col1:
            # Title and description
            title = recommendation.get('title', 'Recommendation')
            st.markdown(f"**{title}**")
            
            description = recommendation.get('description', '')
            if description:
                st.markdown(f"*{description}*")
            
            # Action
            action = recommendation.get('action', '')
            if action:
                st.markdown(f"**Action:** {action}")
        
        with col2:
            # Metrics
            impact = recommendation.get('estimated_impact', '')
            if impact:
                st.markdown(f"📈 **{impact}**")
            
            time_to_fix = recommendation.get('time_to_fix', '')
            if time_to_fix:
                st.markdown(f"⏱️ {time_to_fix}")
            
            difficulty = recommendation.get('difficulty', '')
            if difficulty:
                difficulty_emoji = {
                    'Bardzo łatwe': '🟢',
                    'Łatwe': '🟡', 
                    'Średnie': '🟠',
                    'Trudne': '🔴'
                }.get(difficulty, '⚪')
                st.markdown(f"{difficulty_emoji} {difficulty}")

def display_analysis_history(history: List[Dict]):
    """Display analysis history"""
    
    if not history:
        st.info("No analysis history available.")
        return
    
    for i, analysis in enumerate(reversed(history[-10:])):  # Show last 10
        with st.expander(
            f"{analysis.get('product_name', 'Unknown')} - {analysis.get('analysis_date', 'Unknown')}"
        ):
            results = analysis.get('results', {})
            
            col1, col2, col3 = st.columns(3)
            
            with col1:
                score = results.get('overall_score', 'N/A')
                st.metric("Score", f"{score}/10")
            
            with col2:
                found = results.get('product_found', False)
                status = "Found" if found else "Not Found"
                st.metric("Status", status)
            
            with col3:
                facings = results.get('facing_count', 0)
                st.metric("Facings", facings)
            
            description = results.get('description', '')
            if description:
                st.markdown(f"**Description:** {description[:200]}...")

def create_export_summary(analysis_results: Dict, recommendations: List[Dict], product_name: str) -> str:
    """Create a summary for export purposes"""
    
    summary = f"""
# Shelf Analysis Report - {product_name}

## Analysis Results
- **Overall Score:** {analysis_results.get('overall_score', 'N/A')}/10
- **Product Found:** {'Yes' if analysis_results.get('product_found') else 'No'}
- **Facing Count:** {analysis_results.get('facing_count', 0)}
- **Shelf Position:** {analysis_results.get('shelf_position', 'Unknown')}
- **Price Visible:** {'Yes' if analysis_results.get('price_visible') else 'No'}
- **Product Condition:** {analysis_results.get('product_condition', 'Unknown')}
- **AI Confidence:** {int(analysis_results.get('confidence', 0.5) * 100)}%

## Recommendations ({len(recommendations)})
"""
    
    for i, rec in enumerate(recommendations, 1):
        summary += f"""
### {i}. {rec.get('title', 'Recommendation')}
- **Priority:** {rec.get('priority', 'N/A')}
- **Description:** {rec.get('description', '')}
- **Action:** {rec.get('action', '')}
- **Estimated Impact:** {rec.get('estimated_impact', '')}
- **Time to Fix:** {rec.get('time_to_fix', '')}
- **Difficulty:** {rec.get('difficulty', '')}
"""
    
    return summary