app.py

import torch
import gradio as gr
from transformers import BertTokenizer, BertForSequenceClassification
import matplotlib.pyplot as plt
import numpy as np
from wordcloud import WordCloud
from collections import Counter, defaultdict
import re
import json
import csv
import io
import tempfile
from datetime import datetime
import logging
from functools import lru_cache, wraps
from dataclasses import dataclass
from typing import List, Dict, Optional, Tuple, Any, Callable
from contextlib import contextmanager
import gc
import pandas as pd
from lime.lime_text import LimeTextExplainer  # Added LIME import

@dataclass
class Config:
    MAX_HISTORY_SIZE: int = 1000
    BATCH_SIZE_LIMIT: int = 50
    MAX_TEXT_LENGTH: int = 512
    MIN_WORD_LENGTH: int = 2
    CACHE_SIZE: int = 128
    BATCH_PROCESSING_SIZE: int = 8
    
    # Visualization settings
    FIGURE_SIZE_SINGLE: Tuple[int, int] = (8, 5)
    FIGURE_SIZE_BATCH: Tuple[int, int] = (12, 8)
    WORDCLOUD_SIZE: Tuple[int, int] = (10, 5)
    
    THEMES = {
        'default': {'pos': '#4ecdc4', 'neg': '#ff6b6b'},
        'ocean': {'pos': '#0077be', 'neg': '#ff6b35'},
        'forest': {'pos': '#228b22', 'neg': '#dc143c'},
        'sunset': {'pos': '#ff8c00', 'neg': '#8b0000'}
    }
    
    STOP_WORDS = {
        'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 
        'for', 'of', 'with', 'by', 'is', 'are', 'was', 'were', 'be', 
        'been', 'have', 'has', 'had', 'will', 'would', 'could', 'should'
    }

config = Config()
logger = logging.getLogger(__name__)

# Decorators and Context Managers
def handle_errors(default_return=None):
    """Centralized error handling decorator"""
    def decorator(func: Callable) -> Callable:
        @wraps(func)
        def wrapper(*args, **kwargs):
            try:
                return func(*args, **kwargs)
            except Exception as e:
                logger.error(f"{func.__name__} failed: {e}")
                return default_return if default_return is not None else f"Error: {str(e)}"
        return wrapper
    return decorator

@contextmanager
def managed_figure(*args, **kwargs):
    """Context manager for matplotlib figures to prevent memory leaks"""
    fig = plt.figure(*args, **kwargs)
    try:
        yield fig
    finally:
        plt.close(fig)
        gc.collect()

class ThemeContext:
    """Theme management context"""
    def __init__(self, theme: str = 'default'):
        self.theme = theme
        self.colors = config.THEMES.get(theme, config.THEMES['default'])

# Lazy Model Manager
class ModelManager:
    """Lazy loading model manager"""
    _instance = None
    _model = None
    _tokenizer = None
    _device = None
    
    def __new__(cls):
        if cls._instance is None:
            cls._instance = super().__new__(cls)
        return cls._instance
    
    @property
    def model(self):
        if self._model is None:
            self._load_model()
        return self._model
    
    @property
    def tokenizer(self):
        if self._tokenizer is None:
            self._load_model()
        return self._tokenizer
    
    @property
    def device(self):
        if self._device is None:
            self._device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        return self._device
    
    def _load_model(self):
        """Load model and tokenizer"""
        try:
            self._device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
            self._tokenizer = BertTokenizer.from_pretrained("entropy25/sentimentanalysis")
            self._model = BertForSequenceClassification.from_pretrained("entropy25/sentimentanalysis")
            self._model.to(self._device)
            logger.info(f"Model loaded on {self._device}")
        except Exception as e:
            logger.error(f"Model loading failed: {e}")
            raise

# Simplified Core Classes
class TextProcessor:
    """Optimized text processing"""
    @staticmethod
    @lru_cache(maxsize=config.CACHE_SIZE)
    def clean_text(text: str) -> Tuple[str, ...]:
        """Single-pass text cleaning"""
        words = re.findall(r'\b\w{3,}\b', text.lower())
        return tuple(w for w in words if w not in config.STOP_WORDS)

class HistoryManager:
    """Simplified history management"""
    def __init__(self):
        self._history = []
    
    def add(self, entry: Dict):
        self._history.append({**entry, 'timestamp': datetime.now().isoformat()})
        if len(self._history) > config.MAX_HISTORY_SIZE:
            self._history = self._history[-config.MAX_HISTORY_SIZE:]
    
    def get_all(self) -> List[Dict]:
        return self._history.copy()
    
    def clear(self) -> int:
        count = len(self._history)
        self._history.clear()
        return count
    
    def size(self) -> int:
        return len(self._history)

# Core Analysis Engine
class SentimentEngine:
    """Streamlined sentiment analysis with LIME-based keyword extraction"""
    def __init__(self):
        self.model_manager = ModelManager()
        self.lime_explainer = LimeTextExplainer(class_names=['Negative', 'Positive'])
    
    def predict_proba(self, texts):
        """Prediction function for LIME"""
        if isinstance(texts, str):
            texts = [texts]
        
        inputs = self.model_manager.tokenizer(
            texts, return_tensors="pt", padding=True, 
            truncation=True, max_length=config.MAX_TEXT_LENGTH
        ).to(self.model_manager.device)
        
        with torch.no_grad():
            outputs = self.model_manager.model(**inputs)
            probs = torch.nn.functional.softmax(outputs.logits, dim=-1).cpu().numpy()
        
        return probs
    
    def extract_key_words_lime(self, text: str, top_k: int = 10) -> List[Tuple[str, float]]:
        """Fast keyword extraction using LIME"""
        try:
            # Get LIME explanation
            explanation = self.lime_explainer.explain_instance(
                text, self.predict_proba, num_features=top_k, num_samples=100
            )
            
            # Extract word importance scores
            word_scores = []
            for word, score in explanation.as_list():
                if len(word.strip()) >= config.MIN_WORD_LENGTH:
                    word_scores.append((word.strip().lower(), abs(score)))
            
            # Sort by importance and return top_k
            word_scores.sort(key=lambda x: x[1], reverse=True)
            return word_scores[:top_k]
            
        except Exception as e:
            logger.error(f"LIME extraction failed: {e}")
            return []
    
    def create_heatmap_html(self, text: str, word_scores: Dict[str, float]) -> str:
        """Create HTML heatmap visualization"""
        words = text.split()
        html_parts = ['<div style="font-family: Arial; font-size: 16px; line-height: 1.6;">']
        
        # Normalize scores for color intensity
        if word_scores:
            max_score = max(abs(score) for score in word_scores.values())
            min_score = min(word_scores.values())
        else:
            max_score = min_score = 0
        
        for word in words:
            clean_word = re.sub(r'[^\w]', '', word.lower())
            score = word_scores.get(clean_word, 0)
            
            if score > 0:
                # Positive contribution - green
                intensity = min(255, int(180 * (score / max_score) if max_score > 0 else 0))
                color = f"rgba(0, {intensity}, 0, 0.3)"
            elif score < 0:
                # Negative contribution - red
                intensity = min(255, int(180 * (abs(score) / abs(min_score)) if min_score < 0 else 0))
                color = f"rgba({intensity}, 0, 0, 0.3)"
            else:
                # Neutral - no highlighting
                color = "transparent"
            
            html_parts.append(
                f'<span style="background-color: {color}; padding: 2px; margin: 1px; '
                f'border-radius: 3px;" title="Score: {score:.3f}">{word}</span> '
            )
        
        html_parts.append('</div>')
        return ''.join(html_parts)
    
    @handle_errors(default_return={'sentiment': 'Unknown', 'confidence': 0.0, 'key_words': [], 'heatmap_html': ''})
    def analyze_single(self, text: str) -> Dict:
        """Analyze single text with LIME explanation"""
        if not text.strip():
            raise ValueError("Empty text")
        
        # Get sentiment prediction
        probs = self.predict_proba([text])[0]
        sentiment = "Positive" if probs[1] > probs[0] else "Negative"
        
        # Extract key words using LIME
        key_words = self.extract_key_words_lime(text)
        
        # Create heatmap HTML
        word_scores_dict = dict(key_words)
        heatmap_html = self.create_heatmap_html(text, word_scores_dict)
        
        return {
            'sentiment': sentiment,
            'confidence': float(probs.max()),
            'pos_prob': float(probs[1]),
            'neg_prob': float(probs[0]),
            'key_words': key_words,
            'heatmap_html': heatmap_html
        }
    
    @handle_errors(default_return=[])
    def analyze_batch(self, texts: List[str], progress_callback=None) -> List[Dict]:
        """Optimized batch processing with key words"""
        if len(texts) > config.BATCH_SIZE_LIMIT:
            texts = texts[:config.BATCH_SIZE_LIMIT]
        
        results = []
        batch_size = config.BATCH_PROCESSING_SIZE
        
        for i in range(0, len(texts), batch_size):
            batch = texts[i:i+batch_size]
            
            if progress_callback:
                progress_callback((i + len(batch)) / len(texts))
            
            inputs = self.model_manager.tokenizer(
                batch, return_tensors="pt", padding=True, 
                truncation=True, max_length=config.MAX_TEXT_LENGTH
            ).to(self.model_manager.device)
            
            with torch.no_grad():
                outputs = self.model_manager.model(**inputs)
                probs = torch.nn.functional.softmax(outputs.logits, dim=-1).cpu().numpy()
            
            for text, prob in zip(batch, probs):
                sentiment = "Positive" if prob[1] > prob[0] else "Negative"
                # Extract key words for each text in batch
                key_words = self.extract_key_words(text, top_k=5)  # Fewer for batch processing
                
                results.append({
                    'text': text[:50] + '...' if len(text) > 50 else text,
                    'full_text': text,
                    'sentiment': sentiment,
                    'confidence': float(prob.max()),
                    'pos_prob': float(prob[1]),
                    'neg_prob': float(prob[0]),
                    'key_words': key_words
                })
        
        return results

# Unified Visualization System
class PlotFactory:
    """Factory for creating plots with proper memory management"""
    
    @staticmethod
    @handle_errors(default_return=None)
    def create_sentiment_bars(probs: np.ndarray, theme: ThemeContext) -> plt.Figure:
        """Create sentiment probability bars"""
        with managed_figure(figsize=config.FIGURE_SIZE_SINGLE) as fig:
            ax = fig.add_subplot(111)
            labels = ["Negative", "Positive"]
            colors = [theme.colors['neg'], theme.colors['pos']]
            
            bars = ax.bar(labels, probs, color=colors, alpha=0.8)
            ax.set_title("Sentiment Probabilities", fontweight='bold')
            ax.set_ylabel("Probability")
            ax.set_ylim(0, 1)
            
            # Add value labels
            for bar, prob in zip(bars, probs):
                ax.text(bar.get_x() + bar.get_width()/2., bar.get_height() + 0.02,
                       f'{prob:.3f}', ha='center', va='bottom', fontweight='bold')
            
            fig.tight_layout()
            return fig
    
    @staticmethod
    @handle_errors(default_return=None)
    def create_confidence_gauge(confidence: float, sentiment: str, theme: ThemeContext) -> plt.Figure:
        """Create confidence gauge"""
        with managed_figure(figsize=config.FIGURE_SIZE_SINGLE) as fig:
            ax = fig.add_subplot(111)
            
            # Create gauge
            theta = np.linspace(0, np.pi, 100)
            colors = [theme.colors['neg'] if i < 50 else theme.colors['pos'] for i in range(100)]
            
            for i in range(len(theta)-1):
                ax.fill_between([theta[i], theta[i+1]], [0, 0], [0.8, 0.8], 
                               color=colors[i], alpha=0.7)
            
            # Needle position
            pos = np.pi * (0.5 + (0.4 if sentiment == 'Positive' else -0.4) * confidence)
            ax.plot([pos, pos], [0, 0.6], 'k-', linewidth=6)
            ax.plot(pos, 0.6, 'ko', markersize=10)
            
            ax.set_xlim(0, np.pi)
            ax.set_ylim(0, 1)
            ax.set_title(f'{sentiment} - Confidence: {confidence:.3f}', fontweight='bold')
            ax.set_xticks([0, np.pi/2, np.pi])
            ax.set_xticklabels(['Negative', 'Neutral', 'Positive'])
            ax.axis('off')
            
            fig.tight_layout()
            return fig
    
    @staticmethod
    @handle_errors(default_return=None)
    def create_keyword_chart(key_words: List[Tuple[str, float]], sentiment: str, theme: ThemeContext) -> Optional[plt.Figure]:
        """Create horizontal bar chart for key contributing words"""
        if not key_words:
            return None
        
        with managed_figure(figsize=config.FIGURE_SIZE_SINGLE) as fig:
            ax = fig.add_subplot(111)
            
            words = [word for word, score in key_words]
            scores = [score for word, score in key_words]
            
            # Choose color based on sentiment
            color = theme.colors['pos'] if sentiment == 'Positive' else theme.colors['neg']
            
            # Create horizontal bar chart
            bars = ax.barh(range(len(words)), scores, color=color, alpha=0.7)
            ax.set_yticks(range(len(words)))
            ax.set_yticklabels(words)
            ax.set_xlabel('Attention Weight')
            ax.set_title(f'Top Contributing Words ({sentiment})', fontweight='bold')
            
            # Add value labels on bars
            for i, (bar, score) in enumerate(zip(bars, scores)):
                ax.text(bar.get_width() + 0.001, bar.get_y() + bar.get_height()/2.,
                       f'{score:.3f}', ha='left', va='center', fontsize=9)
            
            # Invert y-axis to show highest scoring word at top
            ax.invert_yaxis()
            ax.grid(axis='x', alpha=0.3)
            fig.tight_layout()
            return fig
    
    @staticmethod
    @handle_errors(default_return=None)
    def create_wordcloud(text: str, sentiment: str, theme: ThemeContext) -> Optional[plt.Figure]:
        """Create word cloud"""
        if len(text.split()) < 3:
            return None
        
        colormap = 'Greens' if sentiment == 'Positive' else 'Reds'
        wc = WordCloud(width=800, height=400, background_color='white',
                      colormap=colormap, max_words=30).generate(text)
        
        with managed_figure(figsize=config.WORDCLOUD_SIZE) as fig:
            ax = fig.add_subplot(111)
            ax.imshow(wc, interpolation='bilinear')
            ax.axis('off')
            ax.set_title(f'{sentiment} Word Cloud', fontweight='bold')
            fig.tight_layout()
            return fig
    
    @staticmethod
    @handle_errors(default_return=None)
    def create_batch_analysis(results: List[Dict], theme: ThemeContext) -> plt.Figure:
        """Create comprehensive batch visualization"""
        with managed_figure(figsize=config.FIGURE_SIZE_BATCH) as fig:
            gs = fig.add_gridspec(2, 2, hspace=0.3, wspace=0.3)
            
            # Sentiment distribution
            ax1 = fig.add_subplot(gs[0, 0])
            sent_counts = Counter([r['sentiment'] for r in results])
            colors = [theme.colors['pos'], theme.colors['neg']]
            ax1.pie(sent_counts.values(), labels=sent_counts.keys(), 
                   autopct='%1.1f%%', colors=colors[:len(sent_counts)])
            ax1.set_title('Sentiment Distribution')
            
            # Confidence histogram
            ax2 = fig.add_subplot(gs[0, 1])
            confs = [r['confidence'] for r in results]
            ax2.hist(confs, bins=8, alpha=0.7, color='skyblue', edgecolor='black')
            ax2.set_title('Confidence Distribution')
            ax2.set_xlabel('Confidence')
            
            # Sentiment over time
            ax3 = fig.add_subplot(gs[1, :])
            pos_probs = [r['pos_prob'] for r in results]
            indices = range(len(results))
            colors_scatter = [theme.colors['pos'] if r['sentiment'] == 'Positive' 
                            else theme.colors['neg'] for r in results]
            ax3.scatter(indices, pos_probs, c=colors_scatter, alpha=0.7, s=60)
            ax3.axhline(y=0.5, color='gray', linestyle='--', alpha=0.5)
            ax3.set_title('Sentiment Progression')
            ax3.set_xlabel('Review Index')
            ax3.set_ylabel('Positive Probability')
            
            return fig


class DataHandler:
    """Handles all data operations"""
    
    @staticmethod
    @handle_errors(default_return=(None, "Export failed"))
    def export_data(data: List[Dict], format_type: str) -> Tuple[Optional[str], str]:
        """Universal data export"""
        if not data:
            return None, "No data to export"
        
        temp_file = tempfile.NamedTemporaryFile(mode='w', delete=False, 
                                               suffix=f'.{format_type}', encoding='utf-8')
        
        if format_type == 'csv':
            writer = csv.writer(temp_file)
            writer.writerow(['Timestamp', 'Text', 'Sentiment', 'Confidence', 'Pos_Prob', 'Neg_Prob', 'Key_Words'])
            for entry in data:
                writer.writerow([
                    entry.get('timestamp', ''),
                    entry.get('text', ''),
                    entry.get('sentiment', ''),
                    f"{entry.get('confidence', 0):.4f}",
                    f"{entry.get('pos_prob', 0):.4f}",
                    f"{entry.get('neg_prob', 0):.4f}",
                    "|".join([f"{word}:{score:.3f}" for word, score in entry.get('key_words', [])])
                ])
        elif format_type == 'json':
            json.dump(data, temp_file, indent=2, ensure_ascii=False)
        
        temp_file.close()
        return temp_file.name, f"Exported {len(data)} entries"
    
    @staticmethod
    @handle_errors(default_return="")
    def process_file(file) -> str:
        """Process uploaded file with improved CSV handling"""
        if not file:
            return ""
        
        try:
            file_path = file.name
            
            if file_path.endswith('.csv'):
                # Try different encodings for CSV files
                for encoding in ['utf-8', 'latin-1', 'cp1252', 'iso-8859-1']:
                    try:
                        # Read CSV with pandas
                        df = pd.read_csv(file_path, encoding=encoding)
                        
                        # Smart column detection - look for text-like columns
                        text_columns = []
                        for col in df.columns:
                            # Check if column contains mostly text (not numbers)
                            sample_values = df[col].dropna().head(10)
                            if len(sample_values) > 0:
                                text_count = sum(1 for val in sample_values 
                                               if isinstance(val, str) and len(str(val).strip()) > 10)
                                if text_count > len(sample_values) * 0.7:  # 70% are text-like
                                    text_columns.append(col)
                        
                        # Use the first text column, or fall back to first column
                        if text_columns:
                            selected_column = text_columns[0]
                        else:
                            selected_column = df.columns[0]
                        
                        # Extract text data
                        reviews = df[selected_column].dropna().astype(str).tolist()
                        
                        # Filter out very short entries and clean
                        cleaned_reviews = []
                        for review in reviews:
                            review = review.strip()
                            if len(review) > 10 and review.lower() != 'nan':  # Filter short/invalid entries
                                cleaned_reviews.append(review)
                        
                        if cleaned_reviews:
                            logger.info(f"Successfully read {len(cleaned_reviews)} reviews from CSV using {encoding} encoding")
                            return '\n'.join(cleaned_reviews)
                            
                    except Exception as e:
                        logger.warning(f"Failed to read CSV with {encoding} encoding: {e}")
                        continue
                
                # If all pandas attempts fail, try basic text processing
                try:
                    with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
                        content = f.read()
                    lines = content.strip().split('\n')
                    
                    # Skip potential header and clean lines
                    cleaned_lines = []
                    for i, line in enumerate(lines):
                        line = line.strip()
                        # Skip first line if it looks like headers
                        if i == 0 and ('review' in line.lower() or 'text' in line.lower() or 'comment' in line.lower()):
                            continue
                        # Remove CSV separators and quotes
                        line = re.sub(r'^[",\']*|[",\']*$', '', line)
                        if len(line) > 10:
                            cleaned_lines.append(line)
                    
                    if cleaned_lines:
                        logger.info(f"Fallback: Successfully read {len(cleaned_lines)} lines from CSV")
                        return '\n'.join(cleaned_lines)
                
                except Exception as e:
                    logger.error(f"Fallback CSV reading failed: {e}")
                
                return "Error: Could not read CSV file. Please check the file format and encoding."
            
            else:
                # Handle text files
                for encoding in ['utf-8', 'latin-1', 'cp1252']:
                    try:
                        with open(file_path, 'r', encoding=encoding) as f:
                            content = f.read().strip()
                        if content:
                            logger.info(f"Successfully read text file with {encoding} encoding")
                            return content
                    except Exception as e:
                        continue
                
                return "Error: Could not read text file. Please check the file encoding."
                
        except Exception as e:
            logger.error(f"File processing error: {e}")
            return f"Error processing file: {str(e)}"

# Main Application
class SentimentApp:
    """Main application orchestrator"""
    
    def __init__(self):
        self.engine = SentimentEngine()
        self.history = HistoryManager()
        self.data_handler = DataHandler()
        
        # Example data
        self.examples = [
            ["While the film's visual effects were undeniably impressive, the story lacked emotional weight, and the pacing felt inconsistent throughout."],
            ["An extraordinary achievement in filmmaking — the direction was masterful, the script was sharp, and every performance added depth and realism."],
            ["Despite a promising start, the film quickly devolved into a series of clichés, with weak character development and an ending that felt rushed and unearned."],
            ["A beautifully crafted story with heartfelt moments and a soundtrack that perfectly captured the emotional tone of each scene."],
            ["The movie was far too long, with unnecessary subplots and dull dialogue that made it difficult to stay engaged until the end."]
        ]

    
    @handle_errors(default_return=("Please enter text", None, None, None, None, None))
    def analyze_single(self, text: str, theme: str = 'default'):
        """Single text analysis with LIME explanation and heatmap"""
        if not text.strip():
            return "Please enter text", None, None, None, None, None
        
        result = self.engine.analyze_single(text)
        
        # Add to history
        self.history.add({
            'text': text[:100],
            'full_text': text,
            **result
        })
        
        # Create visualizations
        theme_ctx = ThemeContext(theme)
        probs = np.array([result['neg_prob'], result['pos_prob']])
        
        prob_plot = PlotFactory.create_sentiment_bars(probs, theme_ctx)
        gauge_plot = PlotFactory.create_confidence_gauge(result['confidence'], result['sentiment'], theme_ctx)
        cloud_plot = PlotFactory.create_wordcloud(text, result['sentiment'], theme_ctx)
        keyword_plot = PlotFactory.create_keyword_chart(result['key_words'], result['sentiment'], theme_ctx)
        
        # Format result text with key words
        key_words_str = ", ".join([f"{word}({score:.3f})" for word, score in result['key_words'][:5]])
        result_text = (f"Sentiment: {result['sentiment']} (Confidence: {result['confidence']:.3f})\n"
                      f"Key Words: {key_words_str}")
        
        # Return heatmap HTML as additional output
        return result_text, prob_plot, gauge_plot, cloud_plot, keyword_plot, result['heatmap_html']
    
    @handle_errors(default_return=None)
    def analyze_batch(self, reviews: str, progress=None):
        """Batch analysis"""
        if not reviews.strip():
            return None
        
        texts = [r.strip() for r in reviews.split('\n') if r.strip()]
        if len(texts) < 2:
            return None
        
        results = self.engine.analyze_batch(texts, progress)
        
        # Add to history
        for result in results:
            self.history.add(result)
        
        # Create visualization
        theme_ctx = ThemeContext('default')
        return PlotFactory.create_batch_analysis(results, theme_ctx)
    
    @handle_errors(default_return=(None, "No history available"))
    def plot_history(self, theme: str = 'default'):
        """Plot analysis history"""
        history = self.history.get_all()
        if len(history) < 2:
            return None, f"Need at least 2 analyses for trends. Current: {len(history)}"
        
        theme_ctx = ThemeContext(theme)
        
        with managed_figure(figsize=(12, 8)) as fig:
            gs = fig.add_gridspec(2, 1, hspace=0.3)
            
            indices = list(range(len(history)))
            pos_probs = [item['pos_prob'] for item in history]
            confs = [item['confidence'] for item in history]
            
            # Sentiment trend
            ax1 = fig.add_subplot(gs[0, 0])
            colors = [theme_ctx.colors['pos'] if p > 0.5 else theme_ctx.colors['neg'] 
                     for p in pos_probs]
            ax1.scatter(indices, pos_probs, c=colors, alpha=0.7, s=60)
            ax1.plot(indices, pos_probs, alpha=0.5, linewidth=2)
            ax1.axhline(y=0.5, color='gray', linestyle='--', alpha=0.5)
            ax1.set_title('Sentiment History')
            ax1.set_ylabel('Positive Probability')
            ax1.grid(True, alpha=0.3)
            
            # Confidence trend
            ax2 = fig.add_subplot(gs[1, 0])
            ax2.bar(indices, confs, alpha=0.7, color='lightblue', edgecolor='navy')
            ax2.set_title('Confidence Over Time')
            ax2.set_xlabel('Analysis Number')
            ax2.set_ylabel('Confidence')
            ax2.grid(True, alpha=0.3)
            
            fig.tight_layout()
            return fig, f"History: {len(history)} analyses"

# Gradio Interface Setup
def create_interface():
    """Create streamlined Gradio interface"""
    app = SentimentApp()
    
    with gr.Blocks(theme=gr.themes.Soft(), title="Movie Sentiment Analyzer") as demo:
        gr.Markdown("# 🎬 AI Movie Sentiment Analyzer")
        gr.Markdown("Optimized sentiment analysis with advanced visualizations and key word extraction")
        
        with gr.Tab("Single Analysis"):
            with gr.Row():
                with gr.Column():
                    text_input = gr.Textbox(
                        label="Movie Review",
                        placeholder="Enter your movie review...",
                        lines=5
                    )
                    with gr.Row():
                        analyze_btn = gr.Button("Analyze", variant="primary")
                        theme_selector = gr.Dropdown(
                            choices=list(config.THEMES.keys()),
                            value="default",
                            label="Theme"
                        )
                    
                    gr.Examples(
                        examples=app.examples,
                        inputs=text_input
                    )
                
                with gr.Column():
                    result_output = gr.Textbox(label="Result", lines=3)
                    heatmap_output = gr.HTML(label="Word Importance Heatmap")
            
            with gr.Row():
                prob_plot = gr.Plot(label="Probabilities")
                gauge_plot = gr.Plot(label="Confidence")
            
            with gr.Row():
                wordcloud_plot = gr.Plot(label="Word Cloud")
                keyword_plot = gr.Plot(label="Key Contributing Words")
        
        with gr.Tab("Batch Analysis"):
            with gr.Row():
                with gr.Column():
                    file_upload = gr.File(label="Upload File", file_types=[".csv", ".txt"])
                    batch_input = gr.Textbox(
                        label="Reviews (one per line)",
                        lines=8
                    )
                
                with gr.Column():
                    load_btn = gr.Button("Load File")
                    batch_btn = gr.Button("Analyze Batch", variant="primary")
            
            batch_plot = gr.Plot(label="Batch Results")
        
        with gr.Tab("History & Export"):
            with gr.Row():
                refresh_btn = gr.Button("Refresh")
                clear_btn = gr.Button("Clear", variant="stop")
                status_btn = gr.Button("Status")
            
            with gr.Row():
                csv_btn = gr.Button("Export CSV")
                json_btn = gr.Button("Export JSON")
            
            history_status = gr.Textbox(label="Status")
            history_plot = gr.Plot(label="History Trends")
            csv_file = gr.File(label="CSV Download", visible=True)
            json_file = gr.File(label="JSON Download", visible=True)
        
        # Event bindings
        analyze_btn.click(
            app.analyze_single,
            inputs=[text_input, theme_selector],
            outputs=[result_output, prob_plot, gauge_plot, wordcloud_plot, keyword_plot, heatmap_output]
        )
        
        load_btn.click(app.data_handler.process_file, inputs=file_upload, outputs=batch_input)
        batch_btn.click(app.analyze_batch, inputs=batch_input, outputs=batch_plot)
        
        refresh_btn.click(
            lambda theme: app.plot_history(theme),
            inputs=theme_selector,
            outputs=[history_plot, history_status]
        )
        
        clear_btn.click(
            lambda: f"Cleared {app.history.clear()} entries",
            outputs=history_status
        )
        
        status_btn.click(
            lambda: f"History: {app.history.size()} entries",
            outputs=history_status
        )
        
        csv_btn.click(
            lambda: app.data_handler.export_data(app.history.get_all(), 'csv'),
            outputs=[csv_file, history_status]
        )
        
        json_btn.click(
            lambda: app.data_handler.export_data(app.history.get_all(), 'json'),
            outputs=[json_file, history_status]
        )
    
    return demo

# Application Entry Point
if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)
    demo = create_interface()
    demo.launch(share=True)