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CEAR / app.py

Update app.py

2ec45a0 verified 10 months ago

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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.
    - 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)

        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,
):
    # 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),
            ("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:
        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(
        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 (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."
        )
    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}**"
        )

    # 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(
        [
            "",
            "### 📝 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\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)

    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:
            eff_df = eff_df.rename(
                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)\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


# ---------------- 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"
        "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.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=[
            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],
    )


if __name__ == "__main__":
    demo.launch()

	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.

	- 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)

	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,
	):
	# 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),
	("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:
	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(
	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 (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."
	)
	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}"
	)

	# 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(
	[
	"",
	"### 📝 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\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)

	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:
	eff_df = eff_df.rename(
	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)\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


	# ---------------- 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"
	"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.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=[
	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],
	)


	if __name__ == "__main__":
	demo.launch()