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 = ['
'] # 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'{word} ' ) html_parts.append('
') 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)