Update app.py

app.py

@@ -1,3 +1,215 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+
+from cear_model import CEARModel
+
+cear_analyzer = CEARModel()
+
+
+def build_dataframe_from_inputs(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]
+    """
+    rows = []
+    for name, minutes, variety in values:
+        minutes = 0.0 if minutes is None else float(minutes)
+        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,
+                }
+            )
+    if not rows:
+        return pd.DataFrame(
+            columns=["platform_name", "minutes_per_week", "variety_score"]
+        )
+    return pd.DataFrame(rows)
+
+
+def analyze_user_data(
+    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,
+    feed_satisfaction,
+    fomo_level,
+):
+    # Build the input DataFrame for the core model
+    df = build_dataframe_from_inputs(
+        [
+            ("tiktok", tiktok_minutes, tiktok_variety),
+            ("instagram", insta_minutes, insta_variety),
+            ("youtube", youtube_minutes, youtube_variety),
+            ("twitter", twitter_minutes, twitter_variety),
+            ("reddit", reddit_minutes, reddit_variety),
+            ("facebook", facebook_minutes, facebook_variety),
+            ("other", other_minutes, other_variety),
+        ]
+    )
+
+    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.",
+            pd.DataFrame(columns=["platform", "efficiency_score"]),
+        )
+
+    # Call core CEAR model
+    scores = cear_analyzer.calculate_scores(
+        df, satisfaction=feed_satisfaction, fomo=fomo_level
+    )
+
+    c = float(scores.get("C_Score", 0.0))
+    a = float(scores.get("A_Risk", 0.0))
+    d = float(scores.get("D_Index", 0.0))
+    avg_variety = scores.get("Avg_Variety", None)
+    satisfaction = scores.get("Satisfaction", None)
+    fomo = scores.get("FOMO", None)
+    per_eff = scores.get("Per_Platform_Efficiency", [])
+
+    # ---------------- Profile based on C & A ---------------- #
+    if c >= 70 and a >= 70:
+        profile = (
+            "You are highly plugged into online culture, but that comes with high "
+            "algorithmic risk and a heavy concentration of attention on a small set of feeds."
+        )
+    elif c >= 70 and a < 70:
+        profile = (
+            "You are well-connected to online culture without extreme algorithmic concentration. "
+            "Your usage is relatively efficient for staying up to date."
+        )
+    elif c < 40 and a >= 70:
+        profile = (
+            "You give a lot of attention to a narrow set of feeds without gaining much cultural exposure. "
+            "This is a high-risk, low-benefit pattern."
+        )
+    else:
+        profile = (
+            "You currently have relatively low exposure to viral trends and also keep algorithmic risk low. "
+            "You are either deliberately detached from viral culture or simply under-invested in trend-dense platforms."
+        )
+
+    # ---------------- 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."
+        )
+    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."
+        )
+    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."
+        )
+    else:
+        variety_text = (
+            f"Your average variety rating is **{avg_variety:.1f} / 10**, indicating a moderate mix of content types."
+        )
+
+    # ---------------- Satisfaction & FOMO interpretation ---------------- #
+    satisfaction_text = ""
+    if satisfaction is not None:
+        if satisfaction <= 3:
+            satisfaction_text = (
+                "You report low satisfaction with your feed, which suggests your current pattern might not "
+                "match what you actually want from social media."
+            )
+        elif satisfaction >= 8:
+            satisfaction_text = (
+                "You report high satisfaction with your feed, indicating your current usage largely aligns "
+                "with what you want out of these platforms."
+            )
+        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."
+            )
+
+    fomo_text = ""
+    if fomo is not None:
+        if fomo >= 7 and c < 50:
+            fomo_text = (
+                "You feel out of the loop and your relatively low C-Score supports that feeling. "
+                "If staying current matters to you, a bit more time on trend-dense platforms could help."
+            )
+        elif fomo <= 3 and c < 40:
+            fomo_text = (
+                "You have limited exposure to trends but do not feel much FOMO, which suggests a comfortable "
+                "distance from viral culture."
+            )
+
+    # ---------------- Summary header ---------------- #
+    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}**",
+    ]
+    if avg_variety is not None:
+        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}**")
+    if fomo is not None:
+        summary_lines.append(f"- **FOMO / Out-of-the-loop (0–10):** **{fomo:.1f}**")
+
+    # ---------------- Interpretation section ---------------- #
+    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.
+"""
+    summary_lines.append("")
+    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. "
+        "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()
+
+    # ---------------- 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:
@@ -6,7 +218,115 @@
             )
         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.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"
+        )
+
+        for _, row in eff_df.iterrows():
+            platform = str(row["platform"])
+            score = float(row["efficiency_score"])
+            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 with low scores cost more attention for less cultural gain."
+        )
+
+        eff_md = "\n".join(lines)
     else:
         eff_df = pd.DataFrame(columns=["platform", "efficiency_score"])
+        eff_md = (
+            "### 📈 Platform efficiency ranking\n\n"
+            "No meaningful screen time was entered, so per-platform efficiency could not be calculated."
+        )
+
+    return summary, eff_md, eff_df
+
+
+# ---------------- Gradio UI ---------------- #
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        "# 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."
+    )
+
+    with gr.Row():
+        with gr.Column():
+            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")
+
+    run_btn.click(
+        fn=analyze_user_data,
+        inputs=[
+            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,
+            feed_satisfaction,
+            fomo_level,
+        ],
+        outputs=[summary_out, eff_md_out, eff_table_out],
+    )
+
 
-    return summary, eff_df
+if __name__ == "__main__":
+    demo.launch()