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text_analyzer.py

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+import re
+import subprocess
+import sys
+
+# Load the spaCy model once globally to keep it lightweight
+def ensure_model(model_name="en_core_web_sm"):
+    try:
+        # Try to load the model
+        import spacy
+        spacy.load(model_name)
+    except OSError:
+        # If the model isn't found, download it
+        print(f"Model '{model_name}' not found. Downloading now...")
+        subprocess.check_call([sys.executable, "-m", "spacy", "download", model_name])
+        print(f"Model '{model_name}' downloaded successfully.")
+
+class TextAnalyzer:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "text": ("STRING", {"multiline": True})
+            }
+        }
+
+    # Define the output types and their names
+    RETURN_TYPES = ("INT", "INT", "INT", "STRING", "STRING", "FLOAT", "STRING", "STRING", "STRING", "STRING", "STRING", "STRING")
+    RETURN_NAMES = ("number_lines", "number_words", "number_characters", "language", "sentiment", "sentiment_polarity", "type", "character", "sentence", "subject", "action", "target")
+
+    FUNCTION = "analyze"
+    CATEGORY = "Bjornulf"
+
+    def analyze(self, text):
+        from langdetect import detect
+        from textblob import TextBlob
+        import spacy
+        
+        ensure_model()
+        nlp = spacy.load("en_core_web_sm")
+        # **Statistics**
+        # Count lines by splitting on newline characters
+        lines = len(text.split('\n'))
+        # Count words by splitting on whitespace
+        words = len(text.split())
+        # Count total characters including spaces and punctuation
+        characters = len(text)
+
+        # **Dialog or Description Detection**
+        # Check if the text starts with a name followed by a colon (e.g., "Jessica:")
+        dialog_match = re.match(r'^([A-Za-z]+):', text)
+        if dialog_match:
+            type_ = 'dialog'
+            character = dialog_match.group(1)  # Extract the character name
+            spoken_text = text[dialog_match.end():].strip()  # Text after the colon
+        else:
+            type_ = 'description'
+            character = None
+            spoken_text = text
+
+        # **Language Detection**
+        try:
+            language = detect(spoken_text)
+        except:
+            language = 'unknown'
+
+        # **Sentiment Analysis**
+        blob = TextBlob(spoken_text)
+        polarity = blob.sentiment.polarity
+        if polarity > 0:
+            sentiment = 'positive'
+        elif polarity < 0:
+            sentiment = 'negative'
+        else:
+            sentiment = 'neutral'
+
+        # **Subject, Action, Target Extraction**
+        # Only perform NLP if the language is English (for spaCy compatibility)
+        if language == 'en':
+            doc = nlp(spoken_text)
+            action = None
+            subject = None
+            target = None
+            # Look for the main verb (action) and its subject and object
+            for token in doc:
+                if token.dep_ == 'ROOT' and token.pos_ == 'VERB':
+                    action = token.text
+                    subject_tokens = [w for w in token.children if w.dep_ == 'nsubj']
+                    target_tokens = [w for w in token.children if w.dep_ == 'dobj']
+                    if subject_tokens:
+                        subject = subject_tokens[0].text
+                    if target_tokens:
+                        target = target_tokens[0].text
+                    break
+        else:
+            subject, action, target = None, None, None
+
+        # **Return Results**
+        # Convert None to empty strings for ComfyUI compatibility
+        return (
+            lines,
+            words,
+            characters,
+            language,
+            sentiment,
+            polarity,
+            type_,
+            character or "",
+            spoken_text or "",
+            subject or "",
+            action or "",
+            target or ""
+        )