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# app.py (Simplified Gradio code)
import gradio as gr
from cear_model import CEARModel
import pandas as pd
# ... (include logic to load PLATFORM_WEIGHTS)
# Instantiate the model globally
cear_analyzer = CEARModel()
def analyze_user_data(input_table):
# 1. Convert Gradio input (list of lists) to DataFrame
user_data_df = pd.DataFrame(input_table, columns=['platform_name', 'minutes_per_week'])
user_data_df['minutes_per_week'] = pd.to_numeric(user_data_df['minutes_per_week'], errors='coerce').fillna(0)
# 2. Call the core model
raw_scores = cear_analyzer.calculate_scores(user_data_df)
# 3. Format output for the user (The "App" layer)
summary = f"""
## 📊 Analysis Summary
- **Cultural Connectedness Score (C-Score):** **{raw_scores['C_Score']:.2f}**
- **Algorithmic Risk Score (A-Risk):** **{raw_scores['A_Risk']:.2f}**
- **Platform Diversity Index (D-Index):** **{raw_scores['D_Index']:.2f}**
---
### 📝 Interpretation
*Your C-Score is based on logarithmically scaled time, reflecting diminishing returns. Your A-Risk is based on raw time, reflecting concentrated attention.*
"""
# Return the formatted string and potentially a table of efficiency
return summary, pd.DataFrame(raw_scores['Per_Platform_Efficiency'])
# Define the Gradio interface
iface = gr.Interface(
fn=analyze_user_data,
inputs=gr.Dataframe(
headers=['platform_name', 'minutes_per_week'],
row_count=5,
col_count=(2, 'fixed'),
label="Weekly Screen Time Input (Source data from OS Tracker)"
),
outputs=[
gr.Markdown(label="Score Results"),
gr.Dataframe(label="Per-Platform Cultural Efficiency")
],
title="CEAR Baseline: Cultural Exposure & Algorithmic Risk Analyzer"
)
iface.launch(server_name="0.0.0.0", server_port=7860) |