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pearl576/thrizll_personality

sourceHugging Faceupdated 1y agoView on Hugging Face

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App README

User Clustering Prediction Model

This Hugging Face Space predicts user clusters based on their characteristics using K-means clustering.

Features

—chat_initiation_rate: Float between 0 and 1
—feedback_score: Integer (1–5)
—avg_chat_length: Integer, depends on search_type
—chat_duration: Integer (minutes), depends on search_type
—search_type: One of ['adventurous', 'creative', 'extrovert', 'introvert', 'organized']
—personality_score: Integer (1–10)

API Usage

You can use this model via API:

python

import requests

def predict_cluster_api(chat_initiation_rate, feedback_score, avg_chat_length, 
                       chat_duration, search_type, personality_score):
    response = requests.post(
        "YOUR_HUGGINGFACE_SPACE_URL/api/predict",
        json={
            "data": [
                chat_initiation_rate, feedback_score, avg_chat_length,
                chat_duration, search_type, personality_score
            ]
        }
    )
    return response.json()

# Example usage
result = predict_cluster_api(0.7, 4, 150, 20, "adventurous", 8)
print(result)