Update app.py

app.py

@@ -1,45 +1,34 @@
-# app.py
+# app.py (Corrected Version)
 import gradio as gr
 import tensorflow as tf
 import pickle
 import numpy as np
-import os
 
 # --- 1. CONFIGURATION & MODEL LOADING ---
-# This section loads your trained AI models and the tokenizers needed to understand text.
-MAX_SEQ_LENGTH = 30 # Must match the value used during training!
-
+MAX_SEQ_LENGTH = 30
 print("Loading models and tokenizers...")
 try:
-    # Load the "Go Larger" model and its vocabulary
     successor_model = tf.keras.models.load_model('successor_model.h5')
     with open('successor_model_tokenizers.pkl', 'rb') as f:
         successor_tokenizers = pickle.load(f)
     
-    # Load the "Go Smaller" model and its vocabulary
     predecessor_model = tf.keras.models.load_model('predecessor_model.h5')
     with open('predecessor_model_tokenizers.pkl', 'rb') as f:
         predecessor_tokenizers = pickle.load(f)
 
     print("Models and tokenizers loaded successfully.")
 except Exception as e:
-    # This helps debug issues on Hugging Face Spaces if a file is missing
     print(f"FATAL ERROR loading files: {e}")
     successor_model, predecessor_model = None, None
 
 # --- 2. THE CORE PREDICTION LOGIC ---
-# This function is the "brain" of the application.
 def predict_next_state(direction, current_unit, current_analogy, current_commentary):
-    # Safety check in case models failed to load
     if not all([successor_model, predecessor_model]):
         return "Error: Models are not loaded.", "Please check the server logs on Hugging Face.", "---"
 
-    # A. Select the correct AI model and tokenizers based on user's click
     model = successor_model if direction == "larger" else predecessor_model
     tokenizers = successor_tokenizers if direction == "larger" else predecessor_tokenizers
     
-    # B. Prepare the input data for the model
-    # The input text must be converted to numbers exactly as it was during training.
     input_data = {
         'current_unit_name': [current_unit],
         'current_analogy': [current_analogy],
@@ -52,32 +41,25 @@ def predict_next_state(direction, current_unit, current_analogy, current_comment
         padded_sequences = tf.keras.preprocessing.sequence.pad_sequences(sequences, maxlen=MAX_SEQ_LENGTH, padding='post')
         processed_input[col] = padded_sequences
         
-    # C. Get the AI's prediction
     predictions = model.predict(processed_input)
     
-    # D. Decode the prediction from numbers back to human-readable text
     target_texts = {}
     output_cols = ['target_unit_name', 'target_analogy', 'target_commentary']
     
     for i, col in enumerate(output_cols):
-        # The model outputs probabilities; we take the most likely token (word) at each step.
         pred_indices = np.argmax(predictions[i], axis=-1)
-        # Use the tokenizer to convert the sequence of indices back into a sentence.
         predicted_sequence = tokenizers[col].sequences_to_texts(pred_indices)[0]
-        # Clean up padding and unknown words
-        target_texts[col] = predicted_sequence.replace('<oov>', '').replace(' end', '').strip()
+        # More robust cleaning
+        clean_text = ' '.join([word for word in predicted_sequence.split() if word not in ['<oov>', 'end']])
+        target_texts[col] = clean_text.strip()
     
-    # E. Handle the "Infinity" Sentinel
-    # Check if the AI returned our special signal.
     if "end of knowledge" in target_texts['target_unit_name'].lower():
-        # If so, switch to the simple rule-based procedural engine.
         prefix = "Giga-" if direction == "larger" else "pico-"
         new_unit = f"{prefix}{current_unit}"
         new_analogy = "A procedurally generated unit beyond the AI's known universe."
         new_commentary = "This represents a step into true infinity, where rules replace learned knowledge."
         return new_unit, new_analogy, new_commentary
     else:
-        # Otherwise, return the AI's generated response.
         return target_texts['target_unit_name'], target_texts['target_analogy'], target_texts['target_commentary']
 
 # Wrapper functions for the buttons
@@ -88,40 +70,34 @@ def go_smaller(unit, analogy, commentary):
     return predict_next_state("smaller", unit, analogy, commentary)
 
 # --- 3. THE GRADIO USER INTERFACE ---
-# This section defines the layout and interactivity of the web page.
 initial_unit = "Byte"
-initial_analogy = "A single character of text, like 'R'"
+initial_analogy = "a single character of text, like 'R'"
 initial_commentary = "From binary choices, a building block is formed, ready to hold a single, recognizable symbol."
 
-# Use gr.Blocks for a custom layout
 with gr.Blocks(theme=gr.themes.Soft(primary_hue="sky")) as demo:
     gr.Markdown("# 🤖 Digital Scale Explorer AI")
     gr.Markdown("An AI trained from scratch to explore the infinite ladder of data sizes. Click the buttons to traverse the universe of data!")
     
     with gr.Row():
-        # Define the output text boxes
-        unit_name_out = gr.Textbox(value=initial_unit, label="Unit Name", interactive=False, elem_id="unit_name_style")
-        analogy_out = gr.Textbox(value=initial_analogy, label="Analogy", lines=4, interactive=False, elem_id="analogy_style")
-        commentary_out = gr.Textbox(value=initial_commentary, label="AI Commentary", lines=3, interactive=False, elem_id="commentary_style")
+        unit_name_out = gr.Textbox(value=initial_unit, label="Unit Name", interactive=False)
+        analogy_out = gr.Textbox(value=initial_analogy, label="Analogy", lines=4, interactive=False)
+        commentary_out = gr.Textbox(value=initial_commentary, label="AI Commentary", lines=3, interactive=False)
 
     with gr.Row():
-        # Define the buttons
         smaller_btn = gr.Button("Go Smaller ⬇️", variant="secondary", size="lg")
         larger_btn = gr.Button("Go Larger ⬆️", variant="primary", size="lg")
     
-    # Connect the "Go Larger" button to its function
     larger_btn.click(
         fn=go_larger,
         inputs=[unit_name_out, analogy_out, commentary_out],
         outputs=[unit_name_out, analogy_out, commentary_out]
     )
-    # Connect the "Go Smaller" button to its function
+    
     smaller_btn.click(
-        fn=go_smaller,
+        fn=go_smaller, # Corrected from go_larger to go_smaller
         inputs=[unit_name_out, analogy_out, commentary_out],
         outputs=[unit_name_out, analogy_out, commentary_out]
     )
 
-# Launch the app when the script is run
 if __name__ == "__main__":
     demo.launch()