Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import matplotlib.pyplot as plt
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+
+# --- Load data ---
+df_games = pd.read_csv("games_data.csv")
+df_sales = pd.read_csv("synthetic_sales_data.csv")
+df_reviews = pd.read_csv("synthetic_game_reviews.csv")
+
+analyzer = SentimentIntensityAnalyzer()
+
+# --- Helper: sales chart for a selected game ---
+def get_sales_chart(title):
+    data = df_sales[df_sales["title"] == title].copy()
+    data["month"] = pd.to_datetime(data["month"])
+    data = data.sort_values("month")
+
+    fig, ax = plt.subplots(figsize=(8, 3))
+    ax.plot(data["month"], data["units_sold"], color="#7c3aed", linewidth=2)
+    ax.fill_between(data["month"], data["units_sold"], alpha=0.15, color="#7c3aed")
+    ax.set_title(f"Monthly Sales — {title}", fontsize=12)
+    ax.set_xlabel("Month")
+    ax.set_ylabel("Units Sold")
+    fig.tight_layout()
+    return fig
+
+# --- Helper: pricing recommendation ---
+def get_recommendation(title):
+    game = df_games[df_games["title"] == title]
+    if game.empty:
+        return "Game not found."
+
+    score = game["metacritic_score"].values[0]
+    price = game["price_eur"].values[0]
+    reviews = df_reviews[df_reviews["title"] == title]["review_text"].tolist()
+    
+    if reviews:
+        avg_sentiment = sum(
+            analyzer.polarity_scores(r)["compound"] for r in reviews
+        ) / len(reviews)
+    else:
+        avg_sentiment = 0
+
+    sales_avg = df_sales[df_sales["title"] == title]["units_sold"].mean()
+
+    # Rule-based recommendation
+    if avg_sentiment > 0.3 and sales_avg > 200:
+        rec = "✅ KEEP PRICE — Strong sentiment and healthy sales."
+    elif avg_sentiment > 0.1 and sales_avg < 100:
+        rec = "📢 RUN PROMOTION — Good reviews but low visibility. Boost with a discount campaign."
+    elif avg_sentiment < -0.1 and sales_avg < 80:
+        rec = "❌ CONSIDER DROPPING — Poor sentiment and weak sales. Not worth shelf space."
+    else:
+        rec = "💛 DISCOUNT — Mixed signals. A price reduction may revive interest."
+
+    return (
+        f"**{title}**\n\n"
+        f"- Metacritic Score: {score}\n"
+        f"- Price: €{price:.2f}\n"
+        f"- Avg Monthly Sales: {sales_avg:.0f} units\n"
+        f"- Avg Sentiment Score: {avg_sentiment:.2f}\n\n"
+        f"### Recommendation: {rec}"
+    )
+
+# --- Build the UI ---
+titles = sorted(df_games["title"].dropna().unique().tolist())
+
+with gr.Blocks(theme=gr.themes.Base(), title="🎮 Game Pricing Dashboard") as demo:
+    gr.Markdown("# 🎮 Video Game Retailer Pricing Dashboard")
+    gr.Markdown("Select a game to view its sales trend and get a pricing recommendation.")
+
+    with gr.Row():
+        dropdown = gr.Dropdown(choices=titles, label="Select a Game", scale=2)
+        btn = gr.Button("Analyse", variant="primary")
+
+    with gr.Row():
+        chart_out = gr.Plot(label="Sales Trend")
+        rec_out = gr.Markdown(label="Pricing Recommendation")
+
+    btn.click(
+        fn=lambda t: (get_sales_chart(t), get_recommendation(t)),
+        inputs=dropdown,
+        outputs=[chart_out, rec_out]
+    )
+
+demo.launch()