Update app.py

app.py

@@ -1,35 +1,66 @@
 import gradio as gr
 import pandas as pd
+import matplotlib.pyplot as plt
 
 df = pd.read_csv("hotel_final_dataset.csv")
 
-hotels = df["hotel_name"].unique().tolist()
+# Optional: sort if month is stored in orderable form
+# If month is text like Jan, Feb, Mar, keep as-is for now
+hotels = sorted(df["hotel_name"].dropna().unique().tolist())
 
 def analyze(hotel):
-    data = df[df["hotel_name"] == hotel]
+    data = df[df["hotel_name"] == hotel].copy()
 
     avg_rating = round(data["avg_rating"].mean(), 2)
     sentiment = round(data["sentiment_score"].mean(), 2)
     occupancy = round(data["occupancy_rate"].mean(), 2)
     price = round(data["price_per_night"].mean(), 2)
     demand = round(data["demand_index"].mean(), 2)
-    recommendation = data["pricing_recommendation"].iloc[0]
-
-    return avg_rating, sentiment, occupancy, price, demand, recommendation
-
-iface = gr.Interface(
-    fn=analyze,
-    inputs=gr.Dropdown(choices=hotels, label="Select Hotel"),
-    outputs=[
-        gr.Text(label="Average Rating"),
-        gr.Text(label="Sentiment Score"),
-        gr.Text(label="Occupancy Rate"),
-        gr.Text(label="Avg Price per Night"),
-        gr.Text(label="Demand Index"),
-        gr.Text(label="Pricing Recommendation")
-    ],
-    title="Hotel Pricing Dashboard",
-    description="AI-powered pricing insights"
-)
-
-iface.launch()
+    recommendation = data["pricing_recommendation"].mode().iloc[0]
+
+    fig, ax = plt.subplots(figsize=(8, 4))
+    ax.plot(data["month"], data["booking_count"], marker="o")
+    ax.set_title("Booking Trend")
+    ax.set_xlabel("Month")
+    ax.set_ylabel("Booking Count")
+    plt.xticks(rotation=45)
+    plt.tight_layout()
+
+    return avg_rating, sentiment, occupancy, price, demand, recommendation, fig
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Hotel Pricing & Demand Dashboard")
+    gr.Markdown("Analyze hotel performance using sentiment, demand, pricing, and booking trends.")
+
+    with gr.Row():
+        hotel_input = gr.Dropdown(choices=hotels, label="Select Hotel", value=hotels[0])
+
+    with gr.Row():
+        avg_rating_output = gr.Textbox(label="Average Rating")
+        sentiment_output = gr.Textbox(label="Sentiment Score")
+        occupancy_output = gr.Textbox(label="Occupancy Rate")
+
+    with gr.Row():
+        price_output = gr.Textbox(label="Average Price per Night")
+        demand_output = gr.Textbox(label="Demand Index")
+        recommendation_output = gr.Textbox(label="Pricing Recommendation")
+
+    plot_output = gr.Plot(label="Booking Trend")
+
+    submit_btn = gr.Button("Run Analysis")
+
+    submit_btn.click(
+        fn=analyze,
+        inputs=hotel_input,
+        outputs=[
+            avg_rating_output,
+            sentiment_output,
+            occupancy_output,
+            price_output,
+            demand_output,
+            recommendation_output,
+            plot_output
+        ]
+    )
+
+demo.launch()