Update app.py

app.py

@@ -8,9 +8,10 @@ cear_analyzer = CEARModel()
 
 
 def build_dataframe_from_inputs(values):
-    """Build a DataFrame from a list of (platform_name, minutes, variety) tuples.
+    """
+    Build a DataFrame from a list of (platform_name, minutes, variety) tuples.
 
-    - If minutes == 0, the row is excluded entirely.
+    - If minutes <= 0, the row is excluded entirely.
     - Variety is only used when minutes > 0.
     """
     rows = []
@@ -19,7 +20,9 @@ def build_dataframe_from_inputs(values):
         if minutes <= 0:
             # Ignore variety when there is no time invested
             continue
+
         variety = None if variety is None else float(variety)
+
         rows.append(
             {
                 "platform_name": name,
@@ -32,6 +35,7 @@ def build_dataframe_from_inputs(values):
         return pd.DataFrame(
             columns=["platform_name", "minutes_per_week", "variety_score"]
         )
+
     return pd.DataFrame(rows)
 
 
@@ -53,7 +57,7 @@ def analyze_user_data(
     feed_satisfaction,
     fomo_level,
 ):
-    # Track impossible input patterns for warnings (variety > 0, minutes == 0)
+    # Track impossible input patterns for warnings (variety > 0, minutes <= 0)
     impossible_platforms = []
 
     def check_impossible(name, minutes, variety):
@@ -87,13 +91,16 @@ def analyze_user_data(
     )
 
     if df.empty:
-        return (
-            "Please enter at least one platform with some weekly minutes.",
-            "### 📈 Platform efficiency ranking
-
-No meaningful screen time was entered, so per-platform efficiency could not be calculated.",
-            pd.DataFrame(columns=["platform", "efficiency_score"]),
+        summary_empty = (
+            "Please enter at least one platform with some weekly minutes."
         )
+        eff_md_empty = (
+            "### 📈 Platform efficiency ranking\n\n"
+            "No meaningful screen time was entered, so per-platform efficiency "
+            "could not be calculated."
+        )
+        empty_df = pd.DataFrame(columns=["platform", "efficiency_score"])
+        return summary_empty, eff_md_empty, empty_df
 
     # Call core CEAR model
     scores = cear_analyzer.calculate_scores(
@@ -133,18 +140,18 @@ No meaningful screen time was entered, so per-platform efficiency could not be c
     # ---------------- Variety interpretation ---------------- #
     if avg_variety is None:
         variety_text = (
-            "You did not provide variety ratings (for platforms with minutes > 0), 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 = (
-            f"Your average variety rating is **{avg_variety:.1f} / 10**, which suggests that your feeds feel "
-            "quite repetitive and reinforce a narrow slice of content."
+            f"Your average variety rating is **{avg_variety:.1f} / 10**, which suggests that your feeds "
+            "feel quite repetitive and reinforce a narrow slice of content."
         )
     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 broadens your exposure and slightly offsets some algorithmic risk."
+            f"Your average variety rating is **{avg_variety:.1f} / 10**, which suggests that you see a wide "
+            "range of topics and styles. This broadens your exposure and slightly offsets some algorithmic risk."
         )
     else:
         variety_text = (
@@ -166,8 +173,8 @@ No meaningful screen time was entered, so per-platform efficiency could not be c
             )
         else:
             satisfaction_text = (
-                "Your satisfaction is in the mid range, which suggests your feed is 'fine' but not fully optimized "
-                "for how you would like to spend your attention."
+                "Your satisfaction is in the mid range, which suggests your feed is \"fine\" but not fully "
+                "optimized for how you would like to spend your attention."
             )
 
     fomo_text = ""
@@ -191,12 +198,19 @@ No meaningful screen time was entered, so per-platform efficiency could not be c
         f"- **Algorithmic Risk Score (A-Risk):** **{a:.2f}**",
         f"- **Platform Diversity Index (D-Index):** **{d:.2f}**",
     ]
+
     if avg_variety is not None:
-        summary_lines.append(f"- **Average Variety Rating (0–10):** **{avg_variety:.2f}**")
+        summary_lines.append(
+            f"- **Average Variety Rating (0–10):** **{avg_variety:.2f}**"
+        )
     if satisfaction is not None:
-        summary_lines.append(f"- **Feed Satisfaction (0–10):** **{satisfaction:.1f}**")
+        summary_lines.append(
+            f"- **Feed Satisfaction (0–10):** **{satisfaction:.1f}**"
+        )
     if fomo is not None:
-        summary_lines.append(f"- **FOMO / Out-of-the-loop (0–10):** **{fomo:.1f}**")
+        summary_lines.append(
+            f"- **FOMO / Out-of-the-loop (0–10):** **{fomo:.1f}**"
+        )
 
     # Impossible input warnings
     if impossible_platforms:
@@ -208,59 +222,67 @@ No meaningful screen time was entered, so per-platform efficiency could not be c
         )
 
     # ---------------- Interpretation section ---------------- #
-    summary_lines.extend([
-        "",
-        "### 📝 Interpretation",
-        "",
-        profile,
-        "",
-        variety_text,
-    ])
+    summary_lines.extend(
+        [
+            "",
+            "### 📝 Interpretation",
+            "",
+            profile,
+            "",
+            variety_text,
+        ]
+    )
+
     if satisfaction_text:
         summary_lines.append("")
         summary_lines.append(satisfaction_text)
+
     if fomo_text:
         summary_lines.append("")
         summary_lines.append(fomo_text)
 
     # ---------------- Survey explainer ---------------- #
-    survey_explainer = """
-### ℹ️ How your answers are used
-
-- **Minutes per week** drive the core scores. More time on high-weight platforms increases both C-Score and A-Risk, with diminishing returns for C-Score.
-- **Per-platform variety (0–10)** is combined into a minutes-weighted average. Low variety means you mainly see one type of content; high variety means you see a wider mix of topics and styles.
-- **Feed satisfaction (0–10)** does not change the scores; it is used to interpret whether your current pattern feels good or frustrating to you.
-- **FOMO (0–10)** is compared with your C-Score: high FOMO with low C-Score means you feel out of the loop, while low FOMO with low C-Score means you are detached by choice.
-"""
+    survey_explainer = (
+        "### ℹ️ How your answers are used\n\n"
+        "- **Minutes per week** drive the core scores. More time on high-weight platforms increases both "
+        "C-Score and A-Risk, with diminishing returns for C-Score.\n"
+        "- **Per-platform variety (0–10)** is combined into a minutes-weighted average. Low variety means you "
+        "mainly see one type of content; high variety means you see a wider mix of topics and styles.\n"
+        "- **Feed satisfaction (0–10)** does not change the scores; it is used to interpret whether your current "
+        "pattern feels good or frustrating to you.\n"
+        "- **FOMO (0–10)** is compared with your C-Score: high FOMO with low C-Score means you feel out of the loop, "
+        "while low FOMO with low C-Score means you are detached by choice."
+    )
+
     summary_lines.append("")
-    summary_lines.append(survey_explainer.strip())
+    summary_lines.append(survey_explainer)
 
     summary_lines.append(
-        "
-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)."
+        "\nThe 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 = "
-".join(summary_lines).strip()
+    summary = "\n".join(summary_lines).strip()
 
     # ---------------- Per-platform efficiency table and explanation ---------------- #
     if isinstance(per_eff, list) and per_eff:
         eff_df = pd.DataFrame(per_eff)
         if "platform_name" in eff_df.columns:
             eff_df = eff_df.rename(
-                columns={"platform_name": "platform", "Cultural_Efficiency": "efficiency_score"}
+                columns={
+                    "platform_name": "platform",
+                    "Cultural_Efficiency": "efficiency_score",
+                }
             )
         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)
-"]
+        lines = ["### 📈 Platform efficiency ranking (0–100)\n"]
         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.
-"
+            "The top platform in your current mix is set to 100 and others are scaled relative to it.\n"
         )
 
         for _, row in eff_df.iterrows():
@@ -269,19 +291,15 @@ The C-Score uses a logarithmic transform of your weekly minutes, encoding dimini
             lines.append(f"- **{platform.capitalize()}**: {score:.1f}")
 
         lines.append(
-            "
-Platforms near 100 are the ones that give you the most cultural exposure per minute in this configuration. "
+            "\nPlatforms 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 = "
-".join(lines)
+        eff_md = "\n".join(lines)
     else:
         eff_df = pd.DataFrame(columns=["platform", "efficiency_score"])
         eff_md = (
-            "### 📈 Platform efficiency ranking
-
-"
+            "### 📈 Platform efficiency ranking\n\n"
             "No meaningful screen time was entered, so per-platform efficiency could not be calculated."
         )
 
@@ -290,6 +308,7 @@ Platforms near 100 are the ones that give you the most cultural exposure per min
 
 # ---------------- Helper functions for reset buttons ---------------- #
 
+
 def reset_pair():
     """Return a pair of zeros for minutes and variety."""
     return 0, 0
@@ -304,8 +323,7 @@ def reset_all():
 
 with gr.Blocks() as demo:
     gr.Markdown(
-        "# CEAR – Cultural Exposure & Algorithmic Risk Analyzer
-"
+        "# CEAR – Cultural Exposure & Algorithmic Risk Analyzer\n"
         "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."
     )
@@ -315,44 +333,100 @@ with gr.Blocks() as demo:
             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_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_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_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_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_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_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_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
@@ -383,16 +457,32 @@ with gr.Blocks() as demo:
             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")
+                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])
+    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(