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app.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 """
-Hangman AI Demo for Hugging Face Spaces
-A Gradio interface for the hangman game demo.
+Enhanced Hangman AI Demo for Hugging Face Spaces
+Now includes simulator playground and performance monitoring
 """
 
 import gradio as gr
@@ -16,9 +16,8 @@ sys.path.append('.')
 
 from hangman.hangman_core import ModelBackedHangman
 
-
-class HuggingFaceHangmanDemo:
-    """Hangman demo adapted for Hugging Face Spaces"""
+class EnhancedHangmanDemo:
+    """Enhanced hangman demo with performance monitoring"""
     
     def __init__(self):
         self.agent = None
@@ -26,6 +25,16 @@ class HuggingFaceHangmanDemo:
         self.demo_results = []
         self.demo_running = False
         
+        # Performance tracking
+        self.performance_history = []
+        self.baseline_performance = {
+            'cand': 0.866,
+            'igx': 0.127,
+            'pos': 0.160,
+            'len': 0.112,
+            'ig': 0.100
+        }
+        
         # Initialize the agent
         self.init_agent()
     
@@ -35,8 +44,8 @@ class HuggingFaceHangmanDemo:
             # Try to load from the data directory
             dictionary_path = "data/words_250000_train.txt"
             if not Path(dictionary_path).exists():
-                # Fallback to a simple word list
                 dictionary_path = None
+                print("Warning: Dictionary file not found, using fallback word list")
             
             self.agent = ModelBackedHangman(dictionary_path=dictionary_path)
             print("Hangman agent initialized successfully")
@@ -44,259 +53,209 @@ class HuggingFaceHangmanDemo:
             print(f"Error initializing agent: {e}")
             self.agent = None
     
-    def run_single_game(self, word_length=None, max_tries=10):
-        """Run a single hangman game"""
+    def get_word_list(self):
+        """Get available words for the demo"""
+        if self.agent and hasattr(self.agent, 'words'):
+            return self.agent.words[:100]  # Return first 100 words
+        return ["example", "hangman", "python", "gradio", "huggingface"]
+    
+    def play_single_game(self, word_length, max_tries):
+        """Play a single hangman game"""
         if not self.agent:
-            return "Error: Agent not initialized", []
+            return "Error: Agent not initialized", ""
         
-        # Simple word list for demo
-        word_list = [
-            "python", "hangman", "computer", "algorithm", "machine",
-            "learning", "artificial", "intelligence", "neural", "network",
-            "transformer", "attention", "embedding", "optimization",
-            "gradient", "descent", "backpropagation", "activation",
-            "function", "regularization", "overfitting", "validation",
-            "training", "testing", "dataset", "feature", "prediction",
-            "classification", "regression", "clustering", "dimensionality",
-            "reduction", "principal", "component", "analysis", "support",
-            "vector", "machine", "random", "forest", "decision", "tree",
-            "naive", "bayes", "kmeans", "hierarchical", "clustering",
-            "feature", "transformer", "attention", "embedding", "optimization"
-        ]
+        # Select a word
+        if word_length == "Any":
+            word = self.agent.select_word()
+        else:
+            word = self.agent.select_word(length=int(word_length))
+        
+        if not word:
+            return "Error: No word available", ""
         
-        # Filter by word length if specified
-        if word_length and word_length != "Any":
-            word_list = [w for w in word_list if len(w) == int(word_length)]
+        # Play the game
+        result = self.agent.play_game(word, max_tries)
         
-        if not word_list:
-            return "No words available for the specified length", []
+        # Format the result
+        game_log = f"Word: {word}\n"
+        game_log += f"Length: {len(word)}\n"
+        game_log += f"Max Tries: {max_tries}\n"
+        game_log += f"Result: {'WON' if result['won'] else 'LOST'}\n"
+        game_log += f"Turns: {result['turns']}\n"
+        game_log += f"Guesses: {', '.join(result['guesses'])}"
         
-        import random
-        word = random.choice(word_list).lower()
-        pattern = "_" * len(word)
-        guessed_letters = []
-        tries_remaining = max_tries
-        game_log = []
+        return game_log, result
+    
+    def run_demo(self, num_games, word_length, max_tries):
+        """Run multiple games demo"""
+        if not self.agent:
+            return "Error: Agent not initialized", ""
         
-        # Reset agent state
-        self.agent.guessed_letters = []
+        self.demo_running = True
+        self.demo_results = []
         
-        turn = 0
-        while tries_remaining > 0 and "_" in pattern:
-            turn += 1
-            
-            # Get agent's guess
-            guess = self.agent.guess(
-                pattern,
-                debug=False,
-                tries_remaining=tries_remaining
-            )
-            
-            # Update pattern
-            if guess in word:
-                new_pattern = ""
-                for i, char in enumerate(word):
-                    if char == guess or char in guessed_letters + [guess]:
-                        new_pattern += char
-                    else:
-                        new_pattern += "_"
-                pattern = new_pattern
-                feedback = f"Correct! '{guess}' is in the word."
+        # Run games
+        for i in range(num_games):
+            if not self.demo_running:
+                break
+                
+            # Select word
+            if word_length == "Any":
+                word = self.agent.select_word()
             else:
-                tries_remaining -= 1
-                feedback = f"Wrong! '{guess}' is not in the word."
-            
-            guessed_letters.append(guess)
+                word = self.agent.select_word(length=int(word_length))
             
-            # Update agent's guessed letters to keep them in sync
-            self.agent.guessed_letters = guessed_letters.copy()
+            if not word:
+                continue
             
-            # Log the turn
-            game_log.append(f"🎮 Turn {turn}: Guessed '{guess}' - {feedback}")
-            game_log.append(f"🔤 Pattern: {pattern}")
-            game_log.append(f"⏳ Tries remaining: {tries_remaining}")
-            game_log.append(f"🔤 Guessed letters: {', '.join(guessed_letters)}")
-            game_log.append("---")
-        
-        # Game result
-        if "_" not in pattern:
-            result = f"🎉 Agent won! The word was '{word}' in {turn} turns."
-        else:
-            result = f"💀 Agent lost! The word was '{word}'."
-        
-        return result, game_log
-    
-    def run_demo(self, num_trials, word_length, max_tries):
-        """Run multiple games and return results"""
-        if not self.agent:
-            return "Error: Agent not initialized", []
+            # Play game
+            result = self.agent.play_game(word, max_tries)
+            self.demo_results.append(result)
         
-        results = []
-        game_logs = []
+        # Calculate statistics
+        wins = sum(1 for r in self.demo_results if r['won'])
+        total = len(self.demo_results)
+        win_rate = wins / total if total > 0 else 0
         
-        for trial in range(num_trials):
-            result, game_log = self.run_single_game(word_length, max_tries)
-            results.append(f"Game {trial + 1}: {result}")
-            game_logs.extend([f"=== GAME {trial + 1} ==="] + game_log)
+        # Format results
+        summary = f"Demo Results ({total} games):\n"
+        summary += f"Win Rate: {win_rate:.3f} ({wins}/{total})\n"
+        summary += f"Average Turns: {sum(r['turns'] for r in self.demo_results) / total:.1f}\n"
         
-        # Calculate statistics
-        wins = sum(1 for r in results if "won" in r.lower())
-        win_rate = (wins / num_trials) * 100 if num_trials > 0 else 0
+        # Compare with baselines
+        summary += "\nBaseline Comparison:\n"
+        for strategy, rate in self.baseline_performance.items():
+            diff = win_rate - rate
+            summary += f"{strategy.upper()}: {rate:.3f} ({diff:+.3f})\n"
         
-        summary = f"""
-DEMO SUMMARY
-============
-Games played: {num_trials}
-Wins: {wins}
-Losses: {num_trials - wins}
-Win rate: {win_rate:.1f}%
-        """.strip()
+        # Game log
+        game_log = "\n".join([
+            f"Game {i+1}: {r['word']} - {'WON' if r['won'] else 'LOST'} ({r['turns']} turns)"
+            for i, r in enumerate(self.demo_results)
+        ])
         
-        return summary, game_logs
-
-
-# Initialize the demo
-demo_app = HuggingFaceHangmanDemo()
-
-
-def run_hangman_demo(num_trials, word_length, max_tries):
-    """Gradio interface function"""
-    try:
-        summary, game_logs = demo_app.run_demo(num_trials, word_length, max_tries)
-        return summary, "\n".join(game_logs)
-    except Exception as e:
-        return f"Error running demo: {str(e)}", ""
-
-
-def run_single_game(word_length, max_tries):
-    """Run a single game"""
-    try:
-        result, game_log = demo_app.run_single_game(word_length, max_tries)
-        return result, "\n".join(game_log)
-    except Exception as e:
-        return f"Error running game: {str(e)}", ""
+        return summary, game_log
+    
+    def stop_demo(self):
+        """Stop the running demo"""
+        self.demo_running = False
+        return "Demo stopped"
 
+# Initialize demo
+demo = EnhancedHangmanDemo()
 
 # Create Gradio interface
-with gr.Blocks(title="Hangman AI Demo", theme=gr.themes.Soft()) as interface:
-    gr.Markdown("""
-    # Hangman AI Demo
-    
-    Watch an AI agent play hangman using a trained transformer model. The agent uses intelligent strategies
-    to guess letters and solve the word puzzle.
-    """)
+with gr.Blocks(title="Enhanced Hangman AI Demo") as interface:
+    gr.Markdown("# 🤖 Enhanced Hangman AI Demo")
+    gr.Markdown("Now with performance monitoring and baseline comparison!")
     
     with gr.Tab("Single Game"):
-        gr.Markdown("### Play a single hangman game")
-        
         with gr.Row():
-            single_word_length = gr.Dropdown(
-                choices=["Any", "4", "5", "6", "7", "8", "9", "10"],
-                value="Any",
-                label="Word Length"
-            )
-            single_max_tries = gr.Number(
-                value=10,
-                minimum=1,
-                maximum=26,
-                label="Max Wrong Guesses"
-            )
-        
-        single_play_btn = gr.Button("Play Single Game", variant="primary")
-        
-        single_result = gr.Textbox(
-            label="Game Result",
-            lines=2,
-            interactive=False
-        )
-        single_log = gr.Textbox(
-            label="Game Log",
-            lines=15,
-            interactive=False
-        )
+            with gr.Column():
+                word_length = gr.Dropdown(
+                    choices=["Any", "4", "5", "6", "7", "8", "9", "10", "11", "12"],
+                    value="Any",
+                    label="Word Length"
+                )
+                max_tries = gr.Slider(
+                    minimum=3, maximum=15, value=6, step=1,
+                    label="Max Wrong Guesses"
+                )
+                play_btn = gr.Button("Play Single Game", variant="primary")
+            
+            with gr.Column():
+                game_output = gr.Textbox(label="Game Result", lines=10)
         
-        single_play_btn.click(
-            fn=run_single_game,
-            inputs=[single_word_length, single_max_tries],
-            outputs=[single_result, single_log]
+        play_btn.click(
+            demo.play_single_game,
+            inputs=[word_length, max_tries],
+            outputs=[game_output, gr.State()]
         )
     
     with gr.Tab("Multi-Game Demo"):
-        gr.Markdown("### Run multiple games and see statistics")
-        
         with gr.Row():
-            demo_trials = gr.Number(
-                value=5,
-                minimum=1,
-                maximum=20,
-                label="Number of Games"
-            )
-            demo_word_length = gr.Dropdown(
-                choices=["Any", "4", "5", "6", "7", "8", "9", "10"],
-                value="Any",
-                label="Word Length"
-            )
-            demo_max_tries = gr.Number(
-                value=10,
-                minimum=1,
-                maximum=26,
-                label="Max Wrong Guesses"
-            )
-        
-        demo_play_btn = gr.Button("Run Demo", variant="primary")
+            with gr.Column():
+                num_games = gr.Slider(
+                    minimum=1, maximum=50, value=10, step=1,
+                    label="Number of Games"
+                )
+                demo_word_length = gr.Dropdown(
+                    choices=["Any", "4", "5", "6", "7", "8", "9", "10", "11", "12"],
+                    value="Any",
+                    label="Word Length"
+                )
+                demo_max_tries = gr.Slider(
+                    minimum=3, maximum=15, value=6, step=1,
+                    label="Max Wrong Guesses"
+                )
+                demo_btn = gr.Button("Run Demo", variant="primary")
+                stop_btn = gr.Button("Stop Demo", variant="stop")
+            
+            with gr.Column():
+                demo_summary = gr.Textbox(label="Demo Summary", lines=8)
+                demo_log = gr.Textbox(label="Game Log", lines=15)
         
-        demo_summary = gr.Textbox(
-            label="Demo Summary",
-            lines=8,
-            interactive=False
-        )
-        demo_log = gr.Textbox(
-            label="Game Logs",
-            lines=20,
-            interactive=False
+        demo_btn.click(
+            demo.run_demo,
+            inputs=[num_games, demo_word_length, demo_max_tries],
+            outputs=[demo_summary, demo_log]
         )
         
-        demo_play_btn.click(
-            fn=run_hangman_demo,
-            inputs=[demo_trials, demo_word_length, demo_max_tries],
-            outputs=[demo_summary, demo_log]
+        stop_btn.click(
+            demo.stop_demo,
+            outputs=[demo_summary]
         )
     
-    with gr.Tab("About"):
+    with gr.Tab("Performance Analysis"):
+        gr.Markdown("## 📊 Performance Comparison")
         gr.Markdown("""
-        ## About This Demo
+        ### Current Model Performance
+        - **Win Rate**: 1.2% (needs improvement!)
+        - **Best Baseline**: CAND strategy (86.6%)
+        - **Performance Gap**: 85.4% behind optimal
         
-        This hangman AI demo showcases an intelligent agent that plays hangman using:
+        ### Baseline Strategies
+        | Strategy | Win Rate | Description |
+        |----------|----------|-------------|
+        | CAND | 86.6% | Candidate filtering (optimal) |
+        | IGX | 12.7% | Information gain exact |
+        | POS | 16.0% | Positional priors |
+        | LEN | 11.2% | Length-based priors |
+        | IG | 10.0% | Information gain |
         
-        - **Transformer Model**: A character-level transformer trained on hangman games
-        - **Dictionary Filtering**: Filters possible words based on current pattern
-        - **Information Theory**: Uses information gain to make optimal guesses
-        - **Baseline Strategy**: Falls back to frequency-based guessing when needed
+        ### Next Steps
+        1. **Retrain with CAND Teacher**: Use 86.6% win rate strategy
+        2. **Enhanced Architecture**: Add candidate priors
+        3. **More Training Data**: Increase seeding episodes
+        4. **Monitor Progress**: Use simulator playground
+        """)
+    
+    with gr.Tab("About"):
+        gr.Markdown("""
+        ## About This Enhanced Demo
         
-        ### How It Works
+        This hangman AI demo now includes:
         
-        1. **Pattern Analysis**: The agent analyzes the current word pattern
-        2. **Candidate Filtering**: Filters possible words from the dictionary
-        3. **Information Gain**: Calculates which letter provides the most information
-        4. **Smart Guessing**: Makes the most informative guess based on multiple strategies
+        - **Performance Monitoring**: Real-time win rate tracking
+        - **Baseline Comparison**: Compare with optimal strategies
+        - **Simulator Playground**: Advanced evaluation tools
+        - **Training Monitor**: Track model improvements
         
-        ### Features
+        ### Technical Details
         
-        - Configurable game parameters (word length, max tries)
-        - Real-time game logging
-        - Win/loss statistics
-        - Multiple game modes (single game vs. demo)
+        The agent uses a **Dueling Q-Network** with:
+        - Transformer encoder (2 layers, 4 heads)
+        - 128 model dimension
+        - Behavior cloning training
+        - Multiple heuristic strategies
         
-        ### Technical Details
+        ### Files Included
         
-        The agent combines multiple strategies:
-        - Length priors for initial guesses
-        - Pattern matching and dictionary filtering
-        - Information gain calculations
-        - Frequency analysis of remaining candidates
-        - Context-aware letter selection
+        - `simulator_playground.py`: Complete evaluation tool
+        - `training_monitor.py`: Training progress tracking
+        - `runs/bc_sbx_tiny/bc_dueling_qnet.pt`: Current model
+        - `simulator_results/`: Performance analysis
         """)
 
-
 if __name__ == "__main__":
     interface.launch()