Update app.py

app.py

@@ -1,207 +1,3 @@
-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):
-    """
-    values: list of tuples [(platform_name, minutes, variety), ...]
-    Returns: DataFrame with 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)
-        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,
-):
-    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.", pd.DataFrame()
-
-    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)
-
-    # --- explanation of how survey inputs are used --- #
-    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 --- #
     if isinstance(per_eff, list) and per_eff:
         eff_df = pd.DataFrame(per_eff)
         if "platform_name" in eff_df.columns:
@@ -214,88 +10,3 @@ def analyze_user_data(
         eff_df = pd.DataFrame(columns=["platform", "efficiency_score"])
 
     return summary, 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_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_out],
-    )
-
-
-if __name__ == "__main__":
-    demo.launch()