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| import joblib | |
| import json | |
| import re | |
| # Load the models | |
| rf_model = joblib.load("personality_rf_model.joblib") | |
| vectorizer = joblib.load("tfidf_vectorizer.joblib") | |
| label_encoder = joblib.load("label_encoder.joblib") | |
| # Load personality profiles | |
| with open("personality_profiles.json", "r") as f: | |
| PERSONALITY_MAP = json.load(f) | |
| # Define dominant types | |
| DOMINANT_TYPES = { | |
| "O": { | |
| "label": "High Openness", | |
| "description": "Curious, imaginative, and open to new experiences. Thrives in creative and unconventional environments." | |
| }, | |
| "C": { | |
| "label": "High Conscientiousness", | |
| "description": "Organized, dependable, and goal-oriented. Strong sense of duty and self-discipline." | |
| }, | |
| "E": { | |
| "label": "High Extraversion", | |
| "description": "Energetic, outgoing, and thrives on social interaction. Feeds off external stimulation." | |
| }, | |
| "A": { | |
| "label": "High Agreeableness", | |
| "description": "Kind-hearted, empathetic, and cooperative. Seeks harmony and avoids conflict." | |
| }, | |
| "N": { | |
| "label": "High Neuroticism", | |
| "description": "Emotionally sensitive, reactive, and prone to mood swings. Deeply introspective." | |
| } | |
| } | |
| def preprocess_text(text): | |
| """Preprocess text for prediction""" | |
| text = text.lower() | |
| text = re.sub(r'[^a-zA-Z\s]', '', text) | |
| text = re.sub(r'\s+', ' ', text) | |
| return text.strip() | |
| def predict_personality(choices_text): | |
| """Predict personality based on choices text""" | |
| try: | |
| # Extract choice numbers from the text | |
| choices = re.findall(r'Step \d+: (\d+)\.', choices_text) | |
| # Define trait mapping for each option number | |
| trait_mapping = { | |
| "1": "O", # Openness | |
| "2": "C", # Conscientiousness | |
| "3": "E", # Extraversion/Agreeableness | |
| "4": "N" # Neuroticism | |
| } | |
| # Initialize trait counts | |
| trait_counts = {"O": 0, "C": 0, "E": 0, "A": 0, "N": 0} | |
| total_choices = len(choices) | |
| # Count traits based on choices | |
| for choice in choices: | |
| if choice in trait_mapping: | |
| trait = trait_mapping[choice] | |
| trait_counts[trait] += 1 | |
| # Special case: option 3 contributes to both E and A | |
| if choice == "3": | |
| trait_counts["A"] += 0.5 # Reduced weight for secondary trait | |
| # Calculate trait scores as percentages | |
| trait_scores = { | |
| trait: count/total_choices | |
| for trait, count in trait_counts.items() | |
| } if total_choices > 0 else trait_counts | |
| # Find dominant trait(s) with stricter criteria | |
| max_score = max(trait_scores.values()) | |
| # Count traits that share the max score | |
| max_score_traits = [ | |
| trait for trait, score in trait_scores.items() | |
| if abs(score - max_score) < 0.01 # Account for floating point comparison | |
| ] | |
| # Only consider dominant if: | |
| # 1. Single trait has highest score | |
| # 2. Score is significantly higher than others (>= 0.5) | |
| # 3. No other trait is close to the max score | |
| if len(max_score_traits) == 1 and max_score >= 0.5: | |
| dominant_trait = max_score_traits[0] | |
| other_scores = [score for trait, score in trait_scores.items() | |
| if trait != dominant_trait] | |
| max_other = max(other_scores) if other_scores else 0 | |
| if max_score - max_other >= 0.2: # Must be significantly higher | |
| profile = DOMINANT_TYPES[dominant_trait] | |
| return { | |
| "type": dominant_trait, | |
| "category": "Dominant Trait", | |
| "label": profile["label"], | |
| "description": profile["description"], | |
| "traits": trait_scores | |
| } | |
| # If no clear dominant trait, use binary code | |
| binary_code = "".join(["H" if trait_scores[trait] > 0.3 else "L" | |
| for trait in ["O", "C", "E", "A", "N"]]) # Fixed order | |
| # Get profile from personality map | |
| profile = PERSONALITY_MAP.get(binary_code, { | |
| "label": "Mixed Profile", | |
| "description": "A balanced combination of different personality traits." | |
| }) | |
| return { | |
| "type": binary_code, | |
| "category": "Mixed Profile", | |
| "label": profile["label"], | |
| "description": profile["description"], | |
| "traits": trait_scores | |
| } | |
| except Exception as e: | |
| print(f"Error during prediction: {e}") | |
| return { | |
| "type": "ERROR", | |
| "category": "Error", | |
| "label": "Prediction Error", | |
| "description": str(e), | |
| "traits": {} | |
| } | |
| # if __name__ == "__main__": | |
| # example_text = "I enjoy meeting new people and trying new experiences" | |
| # result = predict_personality(example_text) | |
| # print(json.dumps(result, indent=2)) |