alter input and output

generate_data.py

@@ -2,7 +2,7 @@ import re
 import json
 
 
-def clean_text_file(input_file, output_file):
+def clean_text_file(input_file):
     """
     Cleans the text file by:
     - Removing lines that start with 'BOOK' or contain only numbers,
@@ -29,12 +29,11 @@ def clean_text_file(input_file, output_file):
         cleaned_lines.append(line)
 
     cleaned_text = "".join(cleaned_lines)
-    chunks = [chunk.strip() for chunk in cleaned_text.split("\n\n\n") if chunk.strip()]
+    poems = [chunk.strip() for chunk in cleaned_text.split("\n\n\n") if chunk.strip()]
 
-    with open(output_file, "w", encoding="utf-8") as outfile:
-        outfile.writelines(cleaned_lines)
-
-    return chunks
+    # in each poem convert any double newlines to single newlines
+    poems = [re.sub(r"\n\n", "\n", poem) for poem in poems]
+    return poems
 
 
 def normalize_whitespace(match):
@@ -43,85 +42,26 @@ def normalize_whitespace(match):
     return "\t" * min(tab_count, 2)
 
 
-def chunk_poem_by_lines(lines, max_chunk_lines, overlap_lines):
-    """
-    Split a poem into chunks of manageable size, preserving lines and overlap for continuity.
-    """
-    chunks = []
-    for i in range(0, len(lines), max_chunk_lines - overlap_lines):
-        chunk = lines[i : i + max_chunk_lines]
-        chunks.append(chunk)
-    return chunks
-
-
-def generate_training_pairs(
-    poems, max_context_lines=10, max_chunk_lines=20, overlap_lines=10
-):
-    """
-    Generate input-output training pairs for poetry generation, using line-based chunking.
-    max_context_lines: The maximum number of lines to consider as context for the next line.
-    max_chunk_lines: The maximum number of lines to consider in a single chunk.
-    overlap_lines: The number of lines to overlap between chunks for continuity.
-
-
-    """
-    training_data = []
-
+def create_training_data(poems, max_lines=7):
+    training_pairs = []
     for poem in poems:
-        lines = poem.splitlines(keepends=True)
+        lines = poem.split("\n")
+        for i in range(len(lines) - 1):
+            prompt = lines[i]
+            continuation = "\n".join(lines[i + 1:i + 1 + max_lines])
+            if continuation.strip():  # Skip empty continuations
+                training_pairs.append({
+                    "input": prompt,
+                    "output": continuation
+                })
+    return training_pairs
 
-        # Chunk the poem into manageable pieces
-        chunks = chunk_poem_by_lines(
-            lines, max_chunk_lines=max_chunk_lines, overlap_lines=overlap_lines
-        )
 
-        for chunk in chunks:
-            for i in range(1, len(chunk)):
-                input_lines = "".join(chunk[max(0, i - max_context_lines) : i])
-                output_line = chunk[i]  # The next line to predict
-                training_data.append({"input": input_lines, "output": output_line})
-
-    return training_data
+# Example usage
+poems = clean_text_file("leaves-of-grass-original.txt")
 
+training_pairs = create_training_data(poems, max_lines=10)
+print(f"Number of training pairs: {len(training_pairs)}")
 
-# Example usage
-poems = clean_text_file("leaves-of-grass-original.txt", "leaves-of-grass-cleaned.txt")
-
-# 400 poems
-print(f"Number of poems: {len(poems)}")
-
-# TODO: Implement generate_training_pairs
-# poems = poems[25:26]  # For testing purposes. Long poem.
-
-
-# Compact Poetry Model
-max_context_lines = 5
-max_chunk_lines = 10
-overlap_lines = 2
-
-"""
-# Narrative Poetry Model
-max_context_lines = 10
-max_chunk_lines = 20
-overlap_lines = 5
-"""
-
-"""
-# Epic or Free Verse Poetry 
-max_context_lines = 20
-max_chunk_lines = 50
-overlap_lines = 10
-"""
-
-training_data = generate_training_pairs(
-    poems,
-    max_context_lines=max_context_lines,
-    max_chunk_lines=max_chunk_lines,
-    overlap_lines=overlap_lines,
-)
-
-print(f"Number of training pairs: {len(training_data)}")
-
-# write the training data to a file
-with open("train.json", "w") as outfile:
-    json.dump(training_data, outfile, indent=2, ensure_ascii=False)
+with open("train.json", "w", encoding="utf-8") as outfile:
+    json.dump(training_pairs, outfile, indent=4, ensure_ascii=False)

leaves-of-grass-original.txt

@@ -1,4 +1,4 @@
-LEAVES OF GRASS
+Leaves of Grass
 
   Come, said my soul,
   Such verses for my Body let us write, (for we are one,)