Update app.py

app.py

@@ -1,49 +1,101 @@
-# 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()
+import gradio as gr
+from cear_model import CEARModel
+import pandas as pd
+
+# Instantiate the model globally
+cear_analyzer = CEARModel()
+
+# --- Configuration: Default Values for Pre-population ---
+# Users will see these rows filled in when the app loads
+DEFAULT_DATA = [
+    ["TikTok", 0],
+    ["Instagram", 0],
+    ["YouTube", 0],
+    ["X/Twitter", 0],
+    ["Reddit", 0],
+    ["Facebook", 0],
+    ["LinkedIn", 0]
+]
+
+def analyze_user_data(input_table, variety_rating, fomo_rating):
+    """
+    Analyzes screen time + subjective survey data.
+    """
+    # 1. Convert Gradio input to DataFrame
+    user_data_df = pd.DataFrame(input_table, columns=['platform_name', 'minutes_per_week'])
+    
+    # Clean inputs: coerce non-numbers to 0
+    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. Generate Analysis Summary
+    # We now integrate the survey scores into the interpretation
+    
+    # Logic for simple feedback based on the new survey inputs
+    variety_status = "High" if variety_rating >= 4 else ("Low" if variety_rating <= 2 else "Moderate")
+    fomo_status = "High" if fomo_rating >= 4 else ("Low" if fomo_rating <= 2 else "Moderate")
+
+    summary = f"""
+    ## 📊 Analysis Summary
+    
+    ### 🧠 Core Metrics
+    - **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}**
+
+    ### 🗣️ Self-Reported Context
+    - **Perceived Variety:** {variety_rating}/5 ({variety_status})
+    - **FOMO Intensity:** {fomo_rating}/5 ({fomo_status})
+
+    ---
+    ### 📝 Interpretation
+    *Your C-Score reflects exposure to viral trends based on logarithmically scaled time. Your A-Risk captures vulnerability to high-intensity feeds.*
+    
+    **Insight:** You reported **{variety_status} variety** and **{fomo_status} FOMO**. 
+    {'⚠️ High FOMO suggests you may be relying on these feeds for social validation despite the algorithmic risk.' if fomo_status == 'High' else '✅ Low FOMO indicates a healthy detachment from the feed cycle.'}
+    """
+
+    # Return the summary text and the efficiency table
+    return summary, pd.DataFrame(raw_scores['Per_Platform_Efficiency'])
+
+# --- Define the Gradio Interface ---
+with gr.Blocks(title="CEAR Model") as demo:
+    gr.Markdown("# 🧭 Cultural Exposure & Algorithmic Risk (CEAR) Model")
+    gr.Markdown("Enter your weekly screen time (minutes) and rate your experience below.")
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            # The Dataframe with pre-populated values
+            input_table = gr.Dataframe(
+                headers=['platform_name', 'minutes_per_week'],
+                value=DEFAULT_DATA,  # <--- THIS PRE-POPULATES THE ROWS
+                datatype=["str", "number"],
+                row_count=7,
+                col_count=(2, 'fixed'),
+                label="Weekly Screen Time (Minutes)"
+            )
+        
+        with gr.Column(scale=1):
+            # New Survey Questions
+            gr.Markdown("### 🧠 Self-Reflection")
+            variety_slider = gr.Slider(1, 5, step=1, value=3, label="How diverse does your feed feel? (1=Repetitive, 5=Diverse)")
+            fomo_slider = gr.Slider(1, 5, step=1, value=3, label="How strong is your FOMO? (1=None, 5=Intense)")
+            
+            analyze_btn = gr.Button("Analyze My Risk", variant="primary")
+
+    with gr.Row():
+        output_text = gr.Markdown(label="Analysis Results")
+        output_table = gr.Dataframe(label="Cultural Efficiency (Score per Minute)")
+
+    # Connect the inputs to the function
+    analyze_btn.click(
+        fn=analyze_user_data,
+        inputs=[input_table, variety_slider, fomo_slider],
+        outputs=[output_text, output_table]
+    )
+
+    # Launch with the required server binding
+    if __name__ == "__main__":
+        demo.launch(server_name="0.0.0.0", server_port=7860)