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README_CCR.txt

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+# CCR Webapp (Stepper Navigation)
+
+## Install
+pip install streamlit pandas numpy
+
+## Run
+streamlit run app.py
+
+## Features
+- Bovenaan **stepper** (segmented control) met 3 stappen i.p.v. tab-switching.
+- Onderaan **Campagne-info**: knop **Volgende →** die naar **Score** gaat.
+- In **Score**: knop **Save rating now** die opslaat en doorstroomt naar **Output**.
+- Brand & Naam naast elkaar, Audience dropdown (met 'Other...'), Channel bevat 'Integrated', Rater dropdown (Lode/Maarten).
+- Live CCR% + YouTube-preview in Score stap (rechts).

app.py

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+
+import os, json, re
+from datetime import date
+import pandas as pd
+import streamlit as st
+
+st.set_page_config(page_title="Culturally Creative & Relevant Rater", page_icon="🗂️", layout="wide")
+
+# --------- Constants ---------
+DATA_DIR = "data"
+RATINGS_CSV = os.path.join(DATA_DIR, "CCR_ratings.csv")
+RESULTS_CSV = os.path.join(DATA_DIR, "CCR_results.csv")
+
+DEFAULT_WEIGHTS = {
+  "CR_cultural_resonance": 0.22,
+  "OR_originality": 0.18,
+  "TI_timeliness": 0.12,
+  "IE_inclusivity_ethics": 0.10,
+  "SH_shareability": 0.15,
+  "BF_brand_channel_fit": 0.13,
+  "CQ_craft_quality": 0.10
+}
+DIMENSIONS = list(DEFAULT_WEIGHTS.keys())
+
+AUDIENCE_OPTIONS = [
+    "BE urban 16-24",
+    "BE Gen Z 18-24",
+    "BE mainstream 25-44",
+    "NL mainstream 25-44",
+    "EU mainstream 25-44",
+    "FR urban 18-34",
+    "DE mainstream 18-49",
+    "Other...",
+]
+CHANNEL_OPTIONS = ["TikTok","Instagram","YouTube","OOH","TV","Radio","Integrated","Other"]
+RATER_OPTIONS = ["Lode","Maarten"]
+
+os.makedirs(DATA_DIR, exist_ok=True)
+
+# --------- Helpers ---------
+def ensure_csv(path):
+    if not os.path.exists(path):
+        cols = ["campaign_id","rater_id","rater_notes","campaign_name","brand","channel","scene_audience","country","submit_date_iso","asset_youtube_url",*DIMENSIONS,"flag_stereotype","flag_misappropriation","flag_sensitive_timing","flag_other_risk","neg_sentiment_ratio_estimate"]
+        pd.DataFrame(columns=cols).to_csv(path, index=False)
+
+def load_ratings():
+    ensure_csv(RATINGS_CSV)
+    try:
+        return pd.read_csv(RATINGS_CSV)
+    except Exception:
+        return pd.DataFrame(columns=["campaign_id","rater_id","rater_notes","campaign_name","brand","channel","scene_audience","country","submit_date_iso","asset_youtube_url",*DIMENSIONS,"flag_stereotype","flag_misappropriation","flag_sensitive_timing","flag_other_risk","neg_sentiment_ratio_estimate"])
+
+def save_rating(row: dict):
+    df = load_ratings()
+    df = pd.concat([df, pd.DataFrame([row])], ignore_index=True)
+    df.to_csv(RATINGS_CSV, index=False)
+
+def to_0_100(x):
+    import pandas as pd
+    return (pd.to_numeric(x, errors="coerce") - 1.0)/4.0*100.0
+
+def compute_results(weights: dict) -> pd.DataFrame:
+    df = load_ratings()
+    if df.empty: 
+        return pd.DataFrame()
+
+    for d in DIMENSIONS:
+        df[f"{d}_100"] = to_0_100(df[d])
+    df["CCR_rater"] = 0.0
+    for d, w in weights.items():
+        df["CCR_rater"] += w * df[f"{d}_100"]
+
+    agg = {**{d: "mean" for d in DIMENSIONS},
+           **{f"{d}_100": "mean" for d in DIMENSIONS},
+           **{c: "mean" for c in ["flag_stereotype","flag_misappropriation","flag_sensitive_timing","flag_other_risk","neg_sentiment_ratio_estimate"] if c in df.columns},
+           "CCR_rater": "mean",
+           "campaign_name":"first",
+           "brand":"first",
+           "channel":"first",
+           "scene_audience":"first",
+           "country":"first",
+           "asset_youtube_url":"first"}
+    grouped = df.groupby("campaign_id", dropna=False).agg(agg).reset_index()
+    grouped["CCR_mean"] = grouped["CCR_rater"]
+    grouped.to_csv(RESULTS_CSV, index=False)
+    return grouped
+
+def live_ccr_preview(scores: dict, weights: dict) -> float:
+    total = 0.0
+    for d,w in weights.items():
+        s100 = (scores.get(d, 3.0) - 1.0)/4.0*100.0
+        total += w * s100
+    return max(0.0, min(100.0, total))
+
+def next_campaign_id(df: pd.DataFrame) -> str:
+    if df.empty or "campaign_id" not in df.columns or df["campaign_id"].dropna().empty:
+        return "CMP001"
+    last = str(df["campaign_id"].dropna().iloc[-1])
+    m = re.match(r"([A-Za-z]*)(\d+)$", last)
+    if m:
+        prefix, num = m.group(1) or "CMP", m.group(2)
+        nxt = int(num) + 1
+        return f"{prefix}{nxt:03d}"
+    return f"CMP{len(df)+1:03d}"
+
+# --------- Session State Defaults ---------
+if "step" not in st.session_state:
+    st.session_state.step = "Campagne-info"
+if "info" not in st.session_state:
+    df0 = load_ratings()
+    st.session_state.info = {
+        "campaign_id": next_campaign_id(df0),
+        "campaign_name": "",
+        "brand": "",
+        "channel": CHANNEL_OPTIONS[0],
+        "scene_audience_choice": AUDIENCE_OPTIONS[0],
+        "scene_audience_custom": "",
+        "country": "BE",
+        "submit_date_iso": str(date.today()),
+        "asset_youtube_url": "",
+        "rater_id": "Lode",
+        "rater_notes": "",
+    }
+if "scores" not in st.session_state:
+    st.session_state.scores = {d: 3.0 for d in DIMENSIONS}
+if "risks" not in st.session_state:
+    st.session_state.risks = {"flag_stereotype":0,"flag_misappropriation":0,"flag_sensitive_timing":0,"flag_other_risk":0,"neg_sentiment_ratio_estimate":0.0}
+
+# --------- Header ---------
+st.title("Culturally Creative & Relevant Rater")
+
+# --------- Stepper (segmented control) ---------
+try:
+    st.session_state.step = st.segmented_control(
+        "Stap",
+        options=["Campagne-info","Score","Output"],
+        default=st.session_state.step,
+    )
+except Exception:
+    # fallback to radio if segmented_control not available
+    st.session_state.step = st.radio("Stap", ["Campagne-info","Score","Output"], index=["Campagne-info","Score","Output"].index(st.session_state.step), horizontal=True)
+
+# --------- RENDER: Campagne-info ---------
+if st.session_state.step == "Campagne-info":
+    st.subheader("Campagne-info")
+    c_left, c_right = st.columns(2)
+    with c_left:
+        st.session_state.info["campaign_id"] = st.text_input("Campaign ID *", value=st.session_state.info["campaign_id"])
+        bn1, bn2 = st.columns(2)
+        with bn1:
+            st.session_state.info["brand"] = st.text_input("Brand", value=st.session_state.info["brand"])
+        with bn2:
+            st.session_state.info["campaign_name"] = st.text_input("Naam", value=st.session_state.info["campaign_name"])
+        st.session_state.info["channel"] = st.selectbox("Channel", CHANNEL_OPTIONS, index=CHANNEL_OPTIONS.index(st.session_state.info["channel"]) if st.session_state.info["channel"] in CHANNEL_OPTIONS else 0)
+    with c_right:
+        st.session_state.info["scene_audience_choice"] = st.selectbox("Scene / Audience", AUDIENCE_OPTIONS, index=AUDIENCE_OPTIONS.index(st.session_state.info["scene_audience_choice"]) if st.session_state.info["scene_audience_choice"] in AUDIENCE_OPTIONS else 0)
+        if st.session_state.info["scene_audience_choice"] == "Other...":
+            st.session_state.info["scene_audience_custom"] = st.text_input("Custom audience", value=st.session_state.info["scene_audience_custom"])
+        st.session_state.info["country"] = st.text_input("Country", value=st.session_state.info["country"])
+        st.session_state.info["submit_date_iso"] = st.text_input("Date (YYYY-MM-DD)", value=st.session_state.info["submit_date_iso"])
+        st.session_state.info["rater_id"] = st.selectbox("Rater", ["Lode","Maarten"], index=["Lode","Maarten"].index(st.session_state.info["rater_id"]) if st.session_state.info["rater_id"] in ["Lode","Maarten"] else 0)
+    st.session_state.info["asset_youtube_url"] = st.text_input("YouTube URL (optioneel)", value=st.session_state.info["asset_youtube_url"], placeholder="https://www.youtube.com/watch?v=...")
+    st.session_state.info["rater_notes"] = st.text_area("Rater notes", value=st.session_state.info["rater_notes"], height=90, placeholder="Kernobservaties, insider cues, etc.")
+
+    if st.button("Volgende →", type="primary"):
+        st.session_state.step = "Score"
+        st.rerun()
+
+# --------- RENDER: Score ---------
+elif st.session_state.step == "Score":
+    st.subheader("Score")
+    DESCRIPTIONS = {
+        "CR_cultural_resonance": {"title":"Cultural Resonance", "desc":"Raakt de campagne de cultuur/scene van de doelgroep? Insider cues, taal, symbolen."},
+        "OR_originality": {"title":"Originality", "desc":"Is het concept verrassend en vernieuwend vs. wat al bestaat?"},
+        "TI_timeliness": {"title":"Timeliness", "desc":"Sluit dit aan bij het momentum: trends, events, seizoen?"},
+        "IE_inclusivity_ethics": {"title":"Inclusivity & Ethics", "desc":"Respectvol en inclusief, zonder stereotypes of toe-eigening?"},
+        "SH_shareability": {"title":"Shareability", "desc":"Hoe deelbaar is het? Hook, quotables, remixedbaarheid."},
+        "BF_brand_channel_fit": {"title":"Brand & Channel Fit", "desc":"Matcht met merkcodes en benut het kanaal optimaal?"},
+        "CQ_craft_quality": {"title":"Craft Quality", "desc":"Sterke uitvoering: beeld/copy/sound/edit/montage."},
+    }
+    left, right = st.columns([1.6, 1])
+    with left:
+        for d in DIMENSIONS:
+            st.markdown(f"**{DESCRIPTIONS[d]['title']}**")
+            st.caption(DESCRIPTIONS[d]['desc'])
+            st.session_state.scores[d] = st.slider("", 1.0, 5.0, st.session_state.scores[d], 0.5, key=f"score_{d}")
+        r1, r2, r3, r4 = st.columns(4)
+        with r1:
+            st.session_state.risks["flag_stereotype"] = int(st.checkbox("Stereotype", value=bool(st.session_state.risks["flag_stereotype"])))
+        with r2:
+            st.session_state.risks["flag_misappropriation"] = int(st.checkbox("Misappropriation", value=bool(st.session_state.risks["flag_misappropriation"])))
+        with r3:
+            st.session_state.risks["flag_sensitive_timing"] = int(st.checkbox("Sensitive timing", value=bool(st.session_state.risks["flag_sensitive_timing"])))
+        with r4:
+            st.session_state.risks["flag_other_risk"] = int(st.checkbox("Other risk", value=bool(st.session_state.risks["flag_other_risk"])))
+        st.session_state.risks["neg_sentiment_ratio_estimate"] = st.slider("Neg sentiment ratio", 0.0, 1.0, float(st.session_state.risks["neg_sentiment_ratio_estimate"]), 0.01)
+    with right:
+        live_ccr = live_ccr_preview(st.session_state.scores, DEFAULT_WEIGHTS)
+        st.subheader("📊 Live Score")
+        st.markdown(f"<div style='font-size:72px;font-weight:800;line-height:1;'> {live_ccr:.0f}% </div>", unsafe_allow_html=True)
+        st.caption("CCR – gewogen (default weights)")
+        if st.session_state.info["asset_youtube_url"]:
+            st.markdown("---")
+            st.caption("YouTube preview")
+            st.video(st.session_state.info["asset_youtube_url"])
+
+    if st.button("Save rating now", type="primary"):
+        info = st.session_state.info
+        scene_audience = info["scene_audience_custom"] if info["scene_audience_choice"] == "Other..." else info["scene_audience_choice"]
+        if not info["campaign_id"].strip():
+            st.error("Campaign ID is verplicht.")
+        else:
+            row = {
+                "campaign_id": info["campaign_id"].strip(),
+                "campaign_name": info["campaign_name"].strip(),
+                "brand": info["brand"].strip(),
+                "channel": info["channel"],
+                "scene_audience": scene_audience.strip(),
+                "country": info["country"].strip(),
+                "submit_date_iso": info["submit_date_iso"].strip(),
+                "asset_youtube_url": info["asset_youtube_url"].strip(),
+                "rater_id": info["rater_id"],
+                "rater_notes": info["rater_notes"],
+                **st.session_state.scores,
+                **st.session_state.risks,
+            }
+            save_rating(row)
+            # increment campaign id for next entry
+            df_after = load_ratings()
+            st.session_state.info["campaign_id"] = next_campaign_id(df_after)
+            st.success("Rating opgeslagen.")
+            # Navigate to Output
+            st.session_state.step = "Output"
+            st.rerun()
+
+# --------- RENDER: Output ---------
+else:  # Output
+    st.subheader("Output")
+    st.markdown("**Dataset (alle ratings)**")
+    st.dataframe(load_ratings(), use_container_width=True)
+    st.markdown("---")
+    st.markdown("**Per-campagne resultaten**")
+    res = compute_results(DEFAULT_WEIGHTS)
+    if not res.empty:
+        st.dataframe(res, use_container_width=True)
+        st.download_button("Download CCR_results.csv", res.to_csv(index=False).encode("utf-8"),
+                           file_name="CCR_results.csv", mime="text/csv")
+    else:
+        st.info("Nog geen resultaten – voeg eerst één of meer ratings toe.")

data/CCR_default_weights.json

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+{
+  "CR_cultural_resonance": 0.22,
+  "OR_originality": 0.18,
+  "TI_timeliness": 0.12,
+  "IE_inclusivity_ethics": 0.1,
+  "SH_shareability": 0.15,
+  "BF_brand_channel_fit": 0.13,
+  "CQ_craft_quality": 0.1
+}

data/CCR_demo_ratings.csv

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+campaign_id,campaign_name,brand,channel,scene_audience,country,submit_date_iso,rater_id,rater_notes,asset_youtube_url,CR_cultural_resonance,OR_originality,TI_timeliness,IE_inclusivity_ethics,SH_shareability,BF_brand_channel_fit,CQ_craft_quality,flag_stereotype,flag_misappropriation,flag_sensitive_timing,flag_other_risk,neg_sentiment_ratio_estimate
+CMP001,Street Sparks 15s,Volt,TikTok,BE urban 16-24,BE,2025-09-01,Lode,"Sterke insider cues, sound is trending.",https://www.youtube.com/watch?v=dQw4w9WgXcQ,4.5,4.0,5.0,4.5,4.0,4.5,4.0,0,0,0,0,0.05
+CMP002,Heritage Remix OOH,Aurora,OOH,EU mainstream 25-44,NL,2025-08-15,Maarten,"Craft top, maar appropriation-risico.",,3.0,3.0,3.0,2.5,2.0,3.0,4.5,0,1,0,0,0.28

data/CCR_ratings.csv

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+campaign_id,rater_id,rater_notes,campaign_name,brand,channel,scene_audience,country,submit_date_iso,asset_youtube_url,CR_cultural_resonance,OR_originality,TI_timeliness,IE_inclusivity_ethics,SH_shareability,BF_brand_channel_fit,CQ_craft_quality,flag_stereotype,flag_misappropriation,flag_sensitive_timing,flag_other_risk,neg_sentiment_ratio_estimate
+CMP001,Lode,,,,TikTok,BE urban 16-24,BE,2025-09-29,,3.0,3.0,3.0,3.0,3.0,3.0,3.0,0,0,0,0,0.0

data/CCR_results.csv

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+campaign_id,CR_cultural_resonance,OR_originality,TI_timeliness,IE_inclusivity_ethics,SH_shareability,BF_brand_channel_fit,CQ_craft_quality,CR_cultural_resonance_100,OR_originality_100,TI_timeliness_100,IE_inclusivity_ethics_100,SH_shareability_100,BF_brand_channel_fit_100,CQ_craft_quality_100,flag_stereotype,flag_misappropriation,flag_sensitive_timing,flag_other_risk,neg_sentiment_ratio_estimate,CCR_rater,campaign_name,brand,channel,scene_audience,country,asset_youtube_url,CCR_mean
+CMP001,3.0,3.0,3.0,3.0,3.0,3.0,3.0,50.0,50.0,50.0,50.0,50.0,50.0,50.0,0.0,0.0,0.0,0.0,0.0,50.0,,,TikTok,BE urban 16-24,BE,,50.0

data/CCR_rubric_template.csv

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+campaign_id,campaign_name,brand,channel,scene_audience,country,submit_date_iso,rater_id,rater_notes,asset_youtube_url,CR_cultural_resonance,OR_originality,TI_timeliness,IE_inclusivity_ethics,SH_shareability,BF_brand_channel_fit,CQ_craft_quality,flag_stereotype,flag_misappropriation,flag_sensitive_timing,flag_other_risk,neg_sentiment_ratio_estimate