Update app.py

app.py

@@ -5,53 +5,105 @@ import plotly.graph_objects as go
 # ── load data ──────────────────────────────────────────────
 df = pd.read_csv("restaurant_master_scored.csv")
 
-CUISINES    = ["All"] + sorted(df["cuisine_type"].unique().tolist())
-PRICES      = ["All"] + ["€", "€€", "€€€", "€€€€"]
-ARRONDS     = ["All"] + sorted(df["arrondissement"].unique().tolist())
+CUISINES = ["All"] + sorted(df["cuisine_type"].unique().tolist())
+PRICES = ["All"] + ["€", "€€", "€€€", "€€€€"]
+ARRONDS = ["All"] + sorted(df["arrondissement"].unique().tolist())
 
 LABEL_COLOR = {"Worth It": "#1A6B3C", "Maybe": "#7D5A00", "Skip It": "#8B1A1A"}
-LABEL_BG    = {"Worth It": "#EAF3DE", "Maybe": "#FAEEDA",  "Skip It": "#FCEBEB"}
+LABEL_BG = {"Worth It": "#EAF3DE", "Maybe": "#FAEEDA", "Skip It": "#FCEBEB"}
 
-# ── score component chart ──────────────────────────────────
-def make_chart(row):
+
+# ── charts ──────────────────────────────────────────────
+def make_chart(row, filtered_df):
+    # First chart: score component chart for top result
     components = {
-        "Sentiment adj":  round(row["sentiment_adj_norm"] * 0.35 * 100, 1),
-        "Price fairness": round(row["price_fairness"]     * 0.25 * 100, 1),
-        "Consistency":    round(row["consistency_index"]  * 0.20 * 100, 1),
-        "Trust":          round(row["trust_score"]        * 0.10 * 100, 1),
-        "Divergence −":   round(-row["star_sentiment_divergence"] * 0.10 * 100, 1),
+        "Sentiment adj": round(row["sentiment_adj_norm"] * 0.35 * 100, 1),
+        "Price fairness": round(row["price_fairness"] * 0.25 * 100, 1),
+        "Consistency": round(row["consistency_index"] * 0.20 * 100, 1),
+        "Trust": round(row["trust_score"] * 0.10 * 100, 1),
+        "Divergence −": round(-row["star_sentiment_divergence"] * 0.10 * 100, 1),
     }
+
     colors = ["#3D2B8E", "#7B6FCA", "#1A6B3C", "#7D5A00", "#8B1A1A"]
-    fig = go.Figure(go.Bar(
-        x=list(components.values()),
-        y=list(components.keys()),
-        orientation="h",
-        marker_color=colors,
-        text=[f"{v:+.1f}" for v in components.values()],
-        textposition="outside",
-    ))
-    fig.update_layout(
+
+    fig_bar = go.Figure(
+        go.Bar(
+            x=list(components.values()),
+            y=list(components.keys()),
+            orientation="h",
+            marker_color=colors,
+            text=[f"{v:+.1f}" for v in components.values()],
+            textposition="outside",
+        )
+    )
+
+    fig_bar.update_layout(
         margin=dict(l=10, r=60, t=10, b=10),
         height=220,
-        xaxis=dict(showgrid=False, zeroline=True,
-                   zerolinecolor="#cccccc", title="Points"),
+        xaxis=dict(
+            showgrid=False,
+            zeroline=True,
+            zerolinecolor="#cccccc",
+            title="Points",
+        ),
         yaxis=dict(showgrid=False),
         paper_bgcolor="rgba(0,0,0,0)",
         plot_bgcolor="rgba(0,0,0,0)",
         font=dict(size=12),
     )
-    return fig
+
+    # Second chart: scatter plot of all filtered restaurants
+    fig_scatter = go.Figure()
+
+    for label in ["Worth It", "Maybe", "Skip It"]:
+        subset = filtered_df[filtered_df["worth_it_label"] == label]
+        if not subset.empty:
+            fig_scatter.add_trace(
+                go.Scatter(
+                    x=subset["star_rating"],
+                    y=subset["true_score"],
+                    mode="markers",
+                    name=label,
+                    marker=dict(
+                        size=10,
+                        color=LABEL_COLOR.get(label, "#666666"),
+                        line=dict(width=1, color="white"),
+                    ),
+                    text=subset["restaurant_name"],
+                    hovertemplate=(
+                        "<b>%{text}</b><br>"
+                        "Stars: %{x:.1f}<br>"
+                        "True Score: %{y:.1f}<br>"
+                        "Verdict: " + label +
+                        "<extra></extra>"
+                    ),
+                )
+            )
+
+    fig_scatter.update_layout(
+        margin=dict(l=10, r=10, t=40, b=10),
+        height=320,
+        xaxis=dict(title="Star rating", showgrid=True, gridcolor="#eeeeee"),
+        yaxis=dict(title="True score", showgrid=True, gridcolor="#eeeeee"),
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font=dict(size=12),
+        legend=dict(orientation="h", y=1.12, x=0),
+    )
+
+    return fig_bar, fig_scatter
+
 
 # ── AI explanation (rule-based, no API key needed) ─────────
 def explain(row):
-    name   = row["restaurant_name"]
-    label  = row["worth_it_label"]
-    score  = row["true_score"]
-    price  = row["price_category"]
-    sent   = row["sentiment_score"]
-    stars  = row["star_rating"]
-    vols   = row["sentiment_volatility"]
-    pf     = row["price_fairness"]
+    name = row["restaurant_name"]
+    label = row["worth_it_label"]
+    score = row["true_score"]
+    price = row["price_category"]
+    sent = row["sentiment_score"]
+    stars = row["star_rating"]
+    vols = row["sentiment_volatility"]
+    pf = row["price_fairness"]
 
     sentiment_word = "positive" if sent > 0.2 else ("neutral" if sent > -0.2 else "negative")
     volatility_note = (
@@ -81,9 +133,11 @@ def explain(row):
         f"and the star rating is {stars:.1f}/5.{volatility_note}{value_note}"
     )
 
+
 # ── main function ──────────────────────────────────────────
 def search(cuisine, price, arrond, top_n):
     filtered = df.copy()
+
     if cuisine != "All":
         filtered = filtered[filtered["cuisine_type"] == cuisine]
     if price != "All":
@@ -92,41 +146,54 @@ def search(cuisine, price, arrond, top_n):
         filtered = filtered[filtered["arrondissement"] == arrond]
 
     if filtered.empty:
-        return "No restaurants match your filters.", None, ""
+        empty_fig = go.Figure()
+        return "No restaurants match your filters.", empty_fig, empty_fig, ""
 
     top = filtered.nlargest(int(top_n), "true_score").reset_index(drop=True)
     best = top.iloc[0]
 
     # Results table
-    display = top[[
-        "restaurant_name", "cuisine_type", "arrondissement",
-        "price_category", "star_rating", "true_score", "worth_it_label"
-    ]].rename(columns={
-        "restaurant_name": "Name",
-        "cuisine_type": "Cuisine",
-        "arrondissement": "Area",
-        "price_category": "Price",
-        "star_rating": "Stars",
-        "true_score": "True Score",
-        "worth_it_label": "Verdict",
-    })
-
-    chart = make_chart(best)
+    display = top[
+        [
+            "restaurant_name",
+            "cuisine_type",
+            "arrondissement",
+            "price_category",
+            "star_rating",
+            "true_score",
+            "worth_it_label",
+        ]
+    ].rename(
+        columns={
+            "restaurant_name": "Name",
+            "cuisine_type": "Cuisine",
+            "arrondissement": "Area",
+            "price_category": "Price",
+            "star_rating": "Stars",
+            "true_score": "True Score",
+            "worth_it_label": "Verdict",
+        }
+    )
+
+    score_chart, scatter_chart = make_chart(best, top)
     explanation = explain(best)
 
-    return display, chart, explanation
+    return display, score_chart, scatter_chart, explanation
+
 
 # ── interface ──────────────────────────────────────────────
 with gr.Blocks(title="Restaurant Worth-It Score — Paris") as demo:
-    gr.Markdown("## Restaurant Worth-It Score — Paris\n"
-                "AI-powered ratings that go beyond the stars. "
-                "Select your filters to find restaurants that truly deliver.")
+    gr.Markdown(
+        "## Restaurant Worth-It Score — Paris\n"
+        "AI-powered ratings that go beyond the stars. "
+        "Select your filters to find restaurants that truly deliver."
+    )
 
     with gr.Row():
         cuisine_dd = gr.Dropdown(CUISINES, value="All", label="Cuisine type")
-        price_dd   = gr.Dropdown(PRICES,   value="All", label="Price tier")
-        arrond_dd  = gr.Dropdown(ARRONDS,  value="All", label="Arrondissement")
-        top_n_sl   = gr.Slider(3, 20, value=10, step=1, label="Results to show")
+        price_dd = gr.Dropdown(PRICES, value="All", label="Price tier")
+        arrond_dd = gr.Dropdown(ARRONDS, value="All", label="Arrondissement")
+        top_n_sl = gr.Slider(3, 20, value=10, step=1, label="Results to show")
 
     search_btn = gr.Button("Find restaurants", variant="primary")
 
@@ -137,14 +204,18 @@ with gr.Blocks(title="Restaurant Worth-It Score — Paris") as demo:
         with gr.Column(scale=1):
             gr.Markdown("#### Score breakdown — top result")
             score_chart = gr.Plot(label="")
+
         with gr.Column(scale=1):
-            gr.Markdown("#### AI explanation — top result")
-            explanation_box = gr.Markdown()
+            gr.Markdown("#### All filtered restaurants")
+            scatter_chart = gr.Plot(label="")
+
+    gr.Markdown("#### AI explanation — top result")
+    explanation_box = gr.Markdown()
 
     search_btn.click(
         fn=search,
         inputs=[cuisine_dd, price_dd, arrond_dd, top_n_sl],
-        outputs=[results_table, score_chart, explanation_box],
+        outputs=[results_table, score_chart, scatter_chart, explanation_box],
     )
 
 demo.launch()