Update app.py

app.py

@@ -1,5 +1,7 @@
 import gradio as gr
 import pandas as pd
+import numpy as np
+
 from cear_model import CEARModel
 
 # Instantiate the core model once
@@ -63,26 +65,34 @@ def analyze_user_data(input_table):
 
     input_table: list of lists from gr.Dataframe, e.g.
         [
-          ["tiktok", 240],
-          ["instagram", 180],
+          ["tiktok", 240, 5],
+          ["instagram", 180, 6],
           ...
         ]
+
     Returns:
         summary_markdown (str), efficiency_dataframe (pd.DataFrame)
     """
-
-    # 1. Basic validation: something must be entered
     if not input_table:
         return "Please enter at least one platform and its weekly minutes.", pd.DataFrame()
 
-    # Convert raw table to DataFrame with fixed columns
-    df = pd.DataFrame(input_table, columns=["platform_name", "minutes_per_week"])
+    # Convert raw table to DataFrame. Support both 2- and 3-column input
+    df = pd.DataFrame(input_table)
+
+    if df.shape[1] == 2:
+        df.columns = ["platform_name", "minutes_per_week"]
+        df["variety_score"] = np.nan
+    else:
+        # Assume 3 columns: platform, minutes, variety
+        df = df.iloc[:, :3]  # ignore any extra accidental columns
+        df.columns = ["platform_name", "minutes_per_week", "variety_score"]
 
-    # Handle types and trim garbage rows
+    # Basic cleaning
     df["platform_name"] = df["platform_name"].astype(str)
     df["minutes_per_week"] = pd.to_numeric(df["minutes_per_week"], errors="coerce")
+    df["variety_score"] = pd.to_numeric(df["variety_score"], errors="coerce")
 
-    # Drop rows where both are missing/empty
+    # Drop fully empty rows
     df = df.dropna(how="all")
     if df.empty:
         return "Please provide at least one platform with some minutes.", pd.DataFrame()
@@ -90,21 +100,35 @@ def analyze_user_data(input_table):
     # Normalize names and minutes
     df["platform_name"] = df["platform_name"].apply(normalize_platform_name)
     df["minutes_per_week"] = df["minutes_per_week"].fillna(0).clip(lower=0)
+    df["variety_score"] = df["variety_score"].clip(lower=0, upper=10)
 
-    # Drop rows with blank platform names
+    # Drop rows with blank names
     df = df[df["platform_name"] != ""]
     if df.empty:
         return "Please provide at least one platform with some minutes.", pd.DataFrame()
 
-    # 2. Call the core CEAR model
-    raw_scores = cear_analyzer.calculate_scores(df)
+    # Compute minutes-weighted average variety (if any variety data present)
+    total_minutes = df["minutes_per_week"].sum()
+    if total_minutes > 0 and df["variety_score"].notna().any():
+        avg_variety = float(
+            np.average(
+                df["variety_score"].fillna(0),
+                weights=df["minutes_per_week"]
+            )
+        )
+    else:
+        avg_variety = None
+
+    # Call the core CEAR model using only the columns it expects
+    df_for_model = df[["platform_name", "minutes_per_week"]].copy()
+    raw_scores = cear_analyzer.calculate_scores(df_for_model)
 
     c = float(raw_scores.get("C_Score", 0.0))
     a = float(raw_scores.get("A_Risk", 0.0))
     d = float(raw_scores.get("D_Index", 0.0))
-    per_eff = raw_scores.get("Per_Platform_Efficiency", {})
+    per_eff = raw_scores.get("Per_Platform_Efficiency", [])
 
-    # 3. Build a human-readable profile based on score bands
+    # Profile based on C and A
     if c >= 70 and a >= 70:
         profile = (
             "You are highly plugged into online culture, but that comes with high "
@@ -126,28 +150,65 @@ def analyze_user_data(input_table):
             "You are either deliberately detached or under-invested in highly trend-dense platforms."
         )
 
-    summary = f"""
-## 📊 CEAR Analysis Summary
-
-- **Cultural Connectedness Score (C-Score):** **{c:.2f}**
-- **Algorithmic Risk Score (A-Risk):** **{a:.2f}**
-- **Platform Diversity Index (D-Index):** **{d:.2f}**
-
-### 📝 Interpretation
+    # Variety interpretation snippet
+    if avg_variety is None:
+        variety_text = (
+            "You did not provide variety ratings, so this analysis focuses only on time and platform mix."
+        )
+    elif avg_variety < 4:
+        variety_text = (
+            f"Your average variety rating is **{avg_variety:.1f} / 10**, which suggests that your feeds feel "
+            "quite repetitive. You may be seeing similar content types despite the time you invest."
+        )
+    elif avg_variety > 7:
+        variety_text = (
+            f"Your average variety rating is **{avg_variety:.1f} / 10**, which suggests that you see a wide range "
+            "of topics and styles. This can broaden your cultural exposure and reduce some perceived stagnation."
+        )
+    else:
+        variety_text = (
+            f"Your average variety rating is **{avg_variety:.1f} / 10**, indicating a moderate mix of content types "
+            "without being extremely narrow or extremely diverse."
+        )
 
-{profile}
+    summary_lines = [
+        "## 📊 CEAR Analysis Summary",
+        "",
+        f"- **Cultural Connectedness Score (C-Score):** **{c:.2f}**",
+        f"- **Algorithmic Risk Score (A-Risk):** **{a:.2f}**",
+        f"- **Platform Diversity Index (D-Index):** **{d:.2f}**",
+    ]
 
-The C-Score is based on a logarithmic transform of your weekly minutes, which bakes in diminishing returns as time increases.  
-A-Risk reflects your raw time investment and how concentrated it is on a small set of high-weight platforms.  
-D-Index captures how spread out your usage is across different platforms.
-""".strip()
+    if avg_variety is not None:
+        summary_lines.append(f"- **Average Variety Rating (0–10):** **{avg_variety:.2f}**")
 
-    # 4. Turn per-platform efficiency into a tidy table
-    if isinstance(per_eff, dict) and per_eff:
-        eff_df = pd.DataFrame(
-            sorted(per_eff.items(), key=lambda kv: kv[1], reverse=True),
-            columns=["platform", "efficiency_score"],
-        )
+    summary_lines.extend(
+        [
+            "",
+            "### 📝 Interpretation",
+            "",
+            profile,
+            "",
+            variety_text,
+            "",
+            "The C-Score is based on a logarithmic transform of your weekly minutes, encoding diminishing "
+            "returns as time increases. A-Risk reflects your raw time investment and how concentrated it is on "
+            "a small set of high-weight platforms. D-Index captures how many platforms you use in a meaningful way "
+            "(higher values mean your time is spread across more platforms).",
+        ]
+    )
+
+    summary = "\n".join(summary_lines).strip()
+
+    # Turn per-platform efficiency into a tidy table
+    if isinstance(per_eff, list) and per_eff:
+        eff_df = pd.DataFrame(per_eff)
+        # Expect columns ['platform_name', 'Cultural_Efficiency']
+        if "platform_name" in eff_df.columns:
+            eff_df = eff_df.rename(
+                columns={"platform_name": "platform", "Cultural_Efficiency": "efficiency_score"}
+            )
+        eff_df = eff_df.sort_values("efficiency_score", ascending=False)
     else:
         eff_df = pd.DataFrame(columns=["platform", "efficiency_score"])
 
@@ -159,16 +220,16 @@ D-Index captures how spread out your usage is across different platforms.
 demo = gr.Interface(
     fn=analyze_user_data,
     inputs=gr.Dataframe(
-        headers=["platform_name", "minutes_per_week"],
+        headers=["platform_name", "minutes_per_week", "variety_score (0–10, optional)"],
         row_count=5,
-        col_count=(2, "fixed"),
+        col_count=(3, "fixed"),
         label="Weekly screen time (by platform)",
         value=[
-            ["tiktok", 240],
-            ["instagram", 180],
-            ["youtube", 120],
-            ["twitter", 60],
-            ["reddit", 90],
+            ["tiktok", 240, 4],
+            ["instagram", 180, 5],
+            ["youtube", 120, 7],
+            ["twitter", 60, 6],
+            ["reddit", 90, 8],
         ],
     ),
     outputs=[
@@ -177,8 +238,8 @@ demo = gr.Interface(
     ],
     title="CEAR – Cultural Exposure & Algorithmic Risk Analyzer",
     description=(
-        "Enter your weekly screen time per platform to estimate your cultural connectedness, "
-        "algorithmic risk, and per-platform efficiency."
+        "Enter your weekly screen time per platform (and optional variety ratings) to estimate your "
+        "cultural connectedness, algorithmic risk, and per-platform efficiency."
     ),
 )