Update app.py

app.py

@@ -8,41 +8,26 @@ cear_analyzer = CEARModel()
 
 
 def build_dataframe_from_inputs(values):
-    """Build DataFrame; ignore variety if minutes == 0."""
-    rows = []
-    for name, minutes, variety in values:
-        minutes = 0.0 if minutes is None else float(minutes)
-        if minutes == 0:
-            variety = None
-        else:
-            variety = None if variety is None else float(variety)
-        if minutes > 0:
-            rows.append({
-                "platform_name": name,
-                "minutes_per_week": minutes,
-                "variety_score": variety,
-            })
-    if not rows:
-        return pd.DataFrame(columns=["platform_name", "minutes_per_week", "variety_score"])
-    return pd.DataFrame(rows)(values):
     """Build a DataFrame from a list of (platform_name, minutes, variety) tuples.
 
-    values: list[tuple[str, float | None, float | None]]
-    Returns: DataFrame with columns [platform_name, minutes_per_week, variety_score]
+    - If minutes == 0, the row is excluded entirely.
+    - Variety is only used when minutes > 0.
     """
     rows = []
     for name, minutes, variety in values:
         minutes = 0.0 if minutes is None else float(minutes)
+        if minutes <= 0:
+            # Ignore variety when there is no time invested
+            continue
         variety = None if variety is None else float(variety)
-        # Keep row if there is any meaningful input
-        if minutes > 0 or (variety is not None and not np.isnan(variety)):
-            rows.append(
-                {
-                    "platform_name": name,
-                    "minutes_per_week": minutes,
-                    "variety_score": variety,
-                }
-            )
+        rows.append(
+            {
+                "platform_name": name,
+                "minutes_per_week": minutes,
+                "variety_score": variety,
+            }
+        )
+
     if not rows:
         return pd.DataFrame(
             columns=["platform_name", "minutes_per_week", "variety_score"]
@@ -68,7 +53,27 @@ def analyze_user_data(
     feed_satisfaction,
     fomo_level,
 ):
-    # Build the input DataFrame for the core model
+    # Track impossible input patterns for warnings (variety > 0, minutes == 0)
+    impossible_platforms = []
+
+    def check_impossible(name, minutes, variety):
+        try:
+            m = 0.0 if minutes is None else float(minutes)
+            v = 0.0 if variety is None else float(variety)
+        except ValueError:
+            return
+        if m <= 0 and v > 0:
+            impossible_platforms.append(name)
+
+    check_impossible("TikTok", tiktok_minutes, tiktok_variety)
+    check_impossible("Instagram", insta_minutes, insta_variety)
+    check_impossible("YouTube", youtube_minutes, youtube_variety)
+    check_impossible("Twitter/X", twitter_minutes, twitter_variety)
+    check_impossible("Reddit", reddit_minutes, reddit_variety)
+    check_impossible("Facebook", facebook_minutes, facebook_variety)
+    check_impossible("Other", other_minutes, other_variety)
+
+    # Build the input DataFrame for the core model (only minutes > 0)
     df = build_dataframe_from_inputs(
         [
             ("tiktok", tiktok_minutes, tiktok_variety),
@@ -84,7 +89,9 @@ def analyze_user_data(
     if df.empty:
         return (
             "Please enter at least one platform with some weekly minutes.",
-            "No meaningful screen time was entered, so per-platform efficiency could not be calculated.",
+            "### 📈 Platform efficiency ranking
+
+No meaningful screen time was entered, so per-platform efficiency could not be calculated.",
             pd.DataFrame(columns=["platform", "efficiency_score"]),
         )
 
@@ -126,7 +133,8 @@ def analyze_user_data(
     # ---------------- Variety interpretation ---------------- #
     if avg_variety is None:
         variety_text = (
-            "You did not provide variety ratings, so this analysis focuses only on time and platform mix."
+            "You did not provide variety ratings (for platforms with minutes > 0), so this analysis "
+            "focuses only on time and platform mix."
         )
     elif avg_variety < 4:
         variety_text = (
@@ -190,6 +198,15 @@ def analyze_user_data(
     if fomo is not None:
         summary_lines.append(f"- **FOMO / Out-of-the-loop (0–10):** **{fomo:.1f}**")
 
+    # Impossible input warnings
+    if impossible_platforms:
+        unique_list = sorted(set(impossible_platforms))
+        joined = ", ".join(unique_list)
+        summary_lines.append(
+            f"- ⚠️ You set a variety score > 0 but 0 minutes for: **{joined}**. "
+            "These variety inputs were ignored in the calculations."
+        )
+
     # ---------------- Interpretation section ---------------- #
     summary_lines.extend([
         "",
@@ -219,12 +236,14 @@ def analyze_user_data(
     summary_lines.append(survey_explainer.strip())
 
     summary_lines.append(
-        "\nThe C-Score uses a logarithmic transform of your weekly minutes, encoding diminishing returns as time increases. "
+        "
+The C-Score uses 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()
+    summary = "
+".join(summary_lines).strip()
 
     # ---------------- Per-platform efficiency table and explanation ---------------- #
     if isinstance(per_eff, list) and per_eff:
@@ -236,10 +255,12 @@ def analyze_user_data(
         eff_df["efficiency_score"] = eff_df["efficiency_score"].round(1)
         eff_df = eff_df.sort_values("efficiency_score", ascending=False)
 
-        lines = ["### 📈 Platform efficiency ranking (0–100)\n"]
+        lines = ["### 📈 Platform efficiency ranking (0–100)
+"]
         lines.append(
             "Higher scores mean more cultural exposure per minute. "
-            "The top platform in your current mix is set to 100 and others are scaled relative to it.\n"
+            "The top platform in your current mix is set to 100 and others are scaled relative to it.
+"
         )
 
         for _, row in eff_df.iterrows():
@@ -248,89 +269,154 @@ def analyze_user_data(
             lines.append(f"- **{platform.capitalize()}**: {score:.1f}")
 
         lines.append(
-            "\nPlatforms near 100 are the ones that give you the most cultural exposure per minute in this configuration. "
+            "
+Platforms near 100 are the ones that give you the most cultural exposure per minute in this configuration. "
             "Platforms with low scores cost more attention for less cultural gain."
         )
 
-        eff_md = "\n".join(lines)
+        eff_md = "
+".join(lines)
     else:
         eff_df = pd.DataFrame(columns=["platform", "efficiency_score"])
         eff_md = (
-            "### 📈 Platform efficiency ranking\n\n"
+            "### 📈 Platform efficiency ranking
+
+"
             "No meaningful screen time was entered, so per-platform efficiency could not be calculated."
         )
 
     return summary, eff_md, eff_df
 
 
+# ---------------- Helper functions for reset buttons ---------------- #
+
+def reset_pair():
+    """Return a pair of zeros for minutes and variety."""
+    return 0, 0
+
+
+def reset_all():
+    """Return zeros for all minutes and variety sliders (7 platforms * 2 values)."""
+    return (0, 0) * 7
+
+
 # ---------------- Gradio UI ---------------- #
 
 with gr.Blocks() as demo:
     gr.Markdown(
-        "# CEAR – Cultural Exposure & Algorithmic Risk Analyzer\n"
+        "# CEAR – Cultural Exposure & Algorithmic Risk Analyzer
+"
         "Enter your weekly screen time per platform, rate the variety of each feed, and optionally report how satisfied "
         "you are with your feed and how much FOMO you feel."
     )
 
-    with gr.Row():
-        with gr.Column():
-            # Reset buttons per platform
-            def reset_tiktok(): return 0, 0
-            def reset_insta(): return 0, 0
-            def reset_youtube(): return 0, 0
-            def reset_twitter(): return 0, 0
-            def reset_reddit(): return 0, 0
-            def reset_facebook(): return 0, 0
-            def reset_other(): return 0, 0
-
-            gr.Markdown("### Weekly minutes & per-platform variety (0–10)")
-            gr.Markdown("### Weekly minutes & per-platform variety (0–10)")
-
-            tiktok_minutes = gr.Number(label="TikTok minutes/week", value=240, precision=0)
-            tiktok_variety = gr.Slider(label="TikTok variety (0–10)", minimum=0, maximum=10, step=1, value=4)
-
-            insta_minutes = gr.Number(label="Instagram minutes/week", value=180, precision=0)
-            insta_variety = gr.Slider(label="Instagram variety (0–10)", minimum=0, maximum=10, step=1, value=5)
-
-            youtube_minutes = gr.Number(label="YouTube minutes/week", value=120, precision=0)
-            youtube_variety = gr.Slider(label="YouTube variety (0–10)", minimum=0, maximum=10, step=1, value=7)
-
-            twitter_minutes = gr.Number(label="Twitter/X minutes/week", value=60, precision=0)
-            twitter_variety = gr.Slider(label="Twitter/X variety (0–10)", minimum=0, maximum=10, step=1, value=6)
-
-            reddit_minutes = gr.Number(label="Reddit minutes/week", value=90, precision=0)
-            reddit_variety = gr.Slider(label="Reddit variety (0–10)", minimum=0, maximum=10, step=1, value=8)
-
-            facebook_minutes = gr.Number(label="Facebook minutes/week", value=45, precision=0)
-            facebook_variety = gr.Slider(label="Facebook variety (0–10)", minimum=0, maximum=10, step=1, value=3)
-
-            other_minutes = gr.Number(label="Other platforms minutes/week", value=30, precision=0)
-            other_variety = gr.Slider(label="Other platforms variety (0–10)", minimum=0, maximum=10, step=1, value=5)
-
-        with gr.Column():
-            gr.Markdown("### Self-report (global)")
-
-            feed_satisfaction = gr.Slider(
-                label="Feed satisfaction (0 = miserable, 10 = very happy)",
-                minimum=0,
-                maximum=10,
-                step=1,
-                value=6,
-            )
-            fomo_level = gr.Slider(
-                label="FOMO / out-of-the-loop feeling (0 = none, 10 = extreme)",
-                minimum=0,
-                maximum=10,
-                step=1,
-                value=4,
-            )
-
-            run_btn = gr.Button("Analyze")
-
-            summary_out = gr.Markdown(label="Score Results")
-            eff_md_out = gr.Markdown(label="Per-platform Efficiency Summary")
-            eff_table_out = gr.Dataframe(label="Per-platform Cultural Efficiency")
+    with gr.Accordion("1. Platform screen time & variety (per platform)", open=True):
+        with gr.Row():
+            with gr.Column():
+                # TikTok row
+                with gr.Row():
+                    tiktok_minutes = gr.Number(label="TikTok minutes/week", value=240, precision=0)
+                    tiktok_variety = gr.Slider(label="TikTok variety (0–10)", minimum=0, maximum=10, step=1, value=4)
+                    tiktok_reset_btn = gr.Button("Reset TikTok")
+
+                # Instagram row
+                with gr.Row():
+                    insta_minutes = gr.Number(label="Instagram minutes/week", value=180, precision=0)
+                    insta_variety = gr.Slider(label="Instagram variety (0–10)", minimum=0, maximum=10, step=1, value=5)
+                    insta_reset_btn = gr.Button("Reset Instagram")
+
+                # YouTube row
+                with gr.Row():
+                    youtube_minutes = gr.Number(label="YouTube minutes/week", value=120, precision=0)
+                    youtube_variety = gr.Slider(label="YouTube variety (0–10)", minimum=0, maximum=10, step=1, value=7)
+                    youtube_reset_btn = gr.Button("Reset YouTube")
+
+                # Twitter/X row
+                with gr.Row():
+                    twitter_minutes = gr.Number(label="Twitter/X minutes/week", value=60, precision=0)
+                    twitter_variety = gr.Slider(label="Twitter/X variety (0–10)", minimum=0, maximum=10, step=1, value=6)
+                    twitter_reset_btn = gr.Button("Reset Twitter/X")
+
+                # Reddit row
+                with gr.Row():
+                    reddit_minutes = gr.Number(label="Reddit minutes/week", value=90, precision=0)
+                    reddit_variety = gr.Slider(label="Reddit variety (0–10)", minimum=0, maximum=10, step=1, value=8)
+                    reddit_reset_btn = gr.Button("Reset Reddit")
+
+                # Facebook row
+                with gr.Row():
+                    facebook_minutes = gr.Number(label="Facebook minutes/week", value=45, precision=0)
+                    facebook_variety = gr.Slider(label="Facebook variety (0–10)", minimum=0, maximum=10, step=1, value=3)
+                    facebook_reset_btn = gr.Button("Reset Facebook")
+
+                # Other row
+                with gr.Row():
+                    other_minutes = gr.Number(label="Other platforms minutes/week", value=30, precision=0)
+                    other_variety = gr.Slider(label="Other platforms variety (0–10)", minimum=0, maximum=10, step=1, value=5)
+                    other_reset_btn = gr.Button("Reset Other")
+
+                # Reset all button
+                reset_all_btn = gr.Button("Reset ALL platforms")
+
+    with gr.Accordion("2. Self-report sliders & results", open=True):
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown("### Self-report (global)")
+
+                feed_satisfaction = gr.Slider(
+                    label="Feed satisfaction (0 = miserable, 10 = very happy)",
+                    minimum=0,
+                    maximum=10,
+                    step=1,
+                    value=6,
+                )
+                fomo_level = gr.Slider(
+                    label="FOMO / out-of-the-loop feeling (0 = none, 10 = extreme)",
+                    minimum=0,
+                    maximum=10,
+                    step=1,
+                    value=4,
+                )
+
+                run_btn = gr.Button("Analyze", variant="primary")
+
+            with gr.Column():
+                summary_out = gr.Markdown(label="Score Results")
+                eff_md_out = gr.Markdown(label="Per-platform Efficiency Summary")
+                eff_table_out = gr.Dataframe(label="Per-platform Cultural Efficiency")
+
+    # Wire up reset buttons (per platform)
+    tiktok_reset_btn.click(reset_pair, inputs=[], outputs=[tiktok_minutes, tiktok_variety])
+    insta_reset_btn.click(reset_pair, inputs=[], outputs=[insta_minutes, insta_variety])
+    youtube_reset_btn.click(reset_pair, inputs=[], outputs=[youtube_minutes, youtube_variety])
+    twitter_reset_btn.click(reset_pair, inputs=[], outputs=[twitter_minutes, twitter_variety])
+    reddit_reset_btn.click(reset_pair, inputs=[], outputs=[reddit_minutes, reddit_variety])
+    facebook_reset_btn.click(reset_pair, inputs=[], outputs=[facebook_minutes, facebook_variety])
+    other_reset_btn.click(reset_pair, inputs=[], outputs=[other_minutes, other_variety])
+
+    # Reset all platforms at once
+    reset_all_btn.click(
+        reset_all,
+        inputs=[],
+        outputs=[
+            tiktok_minutes,
+            tiktok_variety,
+            insta_minutes,
+            insta_variety,
+            youtube_minutes,
+            youtube_variety,
+            twitter_minutes,
+            twitter_variety,
+            reddit_minutes,
+            reddit_variety,
+            facebook_minutes,
+            facebook_variety,
+            other_minutes,
+            other_variety,
+        ],
+    )
 
+    # Run analysis
     run_btn.click(
         fn=analyze_user_data,
         inputs=[