Update app.py

app.py

@@ -1,49 +1,49 @@
-# 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()
+# 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)