text_processing.py•4.39 kB
"""Sample text processing API for Createve.AI API Server."""
class TextAnalyzer:
"""Text analyzer for sentiment and statistics."""
CATEGORY = "text"
@classmethod
def INPUT_TYPES(cls):
"""Define input types for node."""
return {
"required": {
"text": ("STRING", {"multiline": True}),
},
"optional": {
"include_sentiment": ("BOOLEAN", {"default": True}),
"include_statistics": ("BOOLEAN", {"default": True})
}
}
RETURN_TYPES = ("DICT",)
RETURN_NAMES = ("analysis_results",)
FUNCTION = "analyze_text"
def analyze_text(self, text, include_sentiment=True, include_statistics=True):
"""Analyze text for sentiment and statistics."""
result = {}
if include_statistics:
# Basic statistics
words = text.split()
result["statistics"] = {
"character_count": len(text),
"word_count": len(words),
"line_count": len(text.splitlines()),
"average_word_length": sum(len(word) for word in words) / max(len(words), 1)
}
if include_sentiment:
# Very basic sentiment analysis
positive_words = ["good", "great", "excellent", "happy", "positive", "best", "love"]
negative_words = ["bad", "awful", "terrible", "sad", "negative", "worst", "hate"]
lowercase_text = text.lower()
positive_count = sum(lowercase_text.count(word) for word in positive_words)
negative_count = sum(lowercase_text.count(word) for word in negative_words)
if positive_count > negative_count:
sentiment = "positive"
elif negative_count > positive_count:
sentiment = "negative"
else:
sentiment = "neutral"
result["sentiment"] = {
"assessment": sentiment,
"positive_word_count": positive_count,
"negative_word_count": negative_count
}
return (result,)
class TextSummarizer:
"""Text summarizer using extraction-based methods."""
CATEGORY = "text"
@classmethod
def INPUT_TYPES(cls):
"""Define input types for node."""
return {
"required": {
"text": ("STRING", {"multiline": True}),
"summary_length": ("INTEGER", {"default": 3, "min": 1, "max": 10})
}
}
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("summary",)
FUNCTION = "summarize_text"
def summarize_text(self, text, summary_length=3):
"""Summarize text using extraction-based method."""
# Simple extractive text summarization
sentences = [s.strip() for s in text.replace('!', '.').replace('?', '.').split('.') if s.strip()]
if not sentences:
return ("No text to summarize.",)
# Count word frequency
words = text.lower().split()
word_frequency = {}
for word in words:
if len(word) > 3: # Ignore short words
word_frequency[word] = word_frequency.get(word, 0) + 1
# Score sentences
sentence_scores = {}
for i, sentence in enumerate(sentences):
score = 0
for word in sentence.lower().split():
if word in word_frequency:
score += word_frequency[word]
sentence_scores[i] = score
# Get top sentences
top_sentence_indices = sorted(sentence_scores, key=sentence_scores.get, reverse=True)[:summary_length]
top_sentence_indices.sort() # Keep original order
summary = '. '.join(sentences[i] for i in top_sentence_indices)
if not summary.endswith('.'):
summary += '.'
return (summary,)
# Define mappings
NODE_CLASS_MAPPINGS = {
"textAnalyzer": TextAnalyzer,
"textSummarizer": TextSummarizer
}
NODE_DISPLAY_NAME_MAPPINGS = {
"textAnalyzer": "Text Analyzer",
"textSummarizer": "Text Summarizer"
}
API_SERVER_QUEUE_MODE = {
TextSummarizer: True # Only summarizer runs in queue mode as an example
}