Update app.py

app.py

@@ -1,13 +1,128 @@
+import pandas as pd
 import gradio as gr
+import plotly.graph_objects as go
 
-def ping():
-    return "OK"
+# ===============================
+# DATA
+# ===============================
+
+def load_data():
+    try:
+        df = pd.read_csv("merged_summary.csv")
+    except:
+        df = pd.DataFrame([
+            ["Paris", "E-Scooter", 4.6, 4.1, 0.12, 0.06],
+            ["Berlin", "E-Bike", 4.5, 4.0, 0.09, 0.06],
+            ["Madrid", "E-Scooter", 4.2, 4.3, 0.17, 0.05],
+            ["Warsaw", "Shared-EV", 5.0, 4.0, 0.07, 0.05],
+        ], columns=[
+            "city", "vehicle_type", "avg_final_price_eur",
+            "avg_rating", "avg_sentiment", "cancellation_rate"
+        ])
+    return df
+
+
+# ===============================
+# DASHBOARD
+# ===============================
+
+def render_dashboard(city, vehicle):
+    df = load_data()
+
+    if city != "All":
+        df = df[df["city"] == city]
+    if vehicle != "All":
+        df = df[df["vehicle_type"] == vehicle]
+
+    if df.empty:
+        return "No data", go.Figure(), go.Figure(), go.Figure()
+
+    avg_price = df["avg_final_price_eur"].mean()
+    avg_rating = df["avg_rating"].mean()
+    avg_cancel = df["cancellation_rate"].mean()
+
+    kpi = f"""
+    ### KPIs
+    - Avg Price: €{avg_price:.2f}
+    - Avg Rating: {avg_rating:.2f}
+    - Cancellation Rate: {avg_cancel:.2%}
+    """
+
+    fig1 = go.Figure()
+    fig1.add_bar(x=df["city"], y=df["avg_final_price_eur"])
+    fig1.update_layout(title="Average Price")
+
+    fig2 = go.Figure()
+    fig2.add_bar(x=df["city"], y=df["avg_rating"])
+    fig2.update_layout(title="Average Rating")
+
+    fig3 = go.Figure()
+    fig3.add_bar(x=df["city"], y=df["cancellation_rate"])
+    fig3.update_layout(title="Cancellation Rate")
+
+    return kpi, fig1, fig2, fig3
+
+
+# ===============================
+# PREDICTION
+# ===============================
+
+def predict(price, discount):
+    score = 0.5
+    if price < 5:
+        score += 0.2
+    if discount > 10:
+        score += 0.1
+    score = min(max(score, 0), 1)
+
+    return {
+        "satisfaction_probability": round(score, 2),
+        "label": "High" if score > 0.5 else "Low"
+    }
+
+
+# ===============================
+# UI
+# ===============================
 
 with gr.Blocks() as demo:
-    gr.Markdown("# Test Gradio")
-    btn = gr.Button("Ping")
-    out = gr.Textbox()
-    btn.click(ping, outputs=out)
+    gr.Markdown("# Urban Mobility App")
+
+    with gr.Tab("Dashboard"):
+        city = gr.Dropdown(
+            ["All", "Paris", "Berlin", "Madrid", "Warsaw"],
+            value="All",
+            label="City"
+        )
+
+        vehicle = gr.Dropdown(
+            ["All", "E-Scooter", "E-Bike", "Shared-EV"],
+            value="All",
+            label="Vehicle"
+        )
+
+        btn = gr.Button("Refresh")
+
+        kpi = gr.Markdown()
+        chart1 = gr.Plot()
+        chart2 = gr.Plot()
+        chart3 = gr.Plot()
+
+        btn.click(
+            render_dashboard,
+            inputs=[city, vehicle],
+            outputs=[kpi, chart1, chart2, chart3]
+        )
+
+    with gr.Tab("Prediction"):
+        price = gr.Number(label="Price", value=4.0)
+        discount = gr.Number(label="Discount %", value=10)
+
+        btn2 = gr.Button("Predict")
+        out = gr.JSON()
+
+        btn2.click(predict, inputs=[price, discount], outputs=out)
+
 
 if __name__ == "__main__":
     demo.launch()