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dynamic_pricing_bandit_app.py

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+# dynamic_pricing_bandit_app.py
+from datasets import load_dataset
+import pandas as pd
+import numpy as np
+import gradio as gr
+import json
+
+PRICE_POINTS = [5000, 7500, 10000, 12500, 15000]
+SEGMENTS = ["Retail", "HNI", "Corporate"]
+DEAL_TYPES = ["M&A Advisory", "Debt Issuance", "Equity Offering", "Restructuring"]
+REGIONS = ["North America", "Europe", "Asia Pacific", "Latin America", "Middle East"]
+INDUSTRIES = ["Technology", "Healthcare", "Financial Services", "Energy", "Consumer Goods", "Industrial"]
+
+class ThompsonBandit:
+    def __init__(self, n_arms):
+        self.successes = np.ones(n_arms)
+        self.failures = np.ones(n_arms)
+
+    def select_arm(self):
+        return np.argmax(np.random.beta(self.successes, self.failures))
+
+    def update(self, arm, reward):
+        if reward:
+            self.successes[arm] += 1
+        else:
+            self.failures[arm] += 1
+
+# Load HF dataset
+dataset = load_dataset("banking77", split="train[:500]")
+df = pd.DataFrame(dataset)
+df["segment"] = np.random.choice(SEGMENTS, len(df))
+df["deal_type"] = np.random.choice(DEAL_TYPES, len(df))
+df["deal_size"] = np.random.lognormal(mean=16, sigma=1.0, size=len(df)).astype(int)
+df["region"] = np.random.choice(REGIONS, len(df))
+df["industry"] = np.random.choice(INDUSTRIES, len(df))
+
+bandit = ThompsonBandit(len(PRICE_POINTS))
+
+def recommend_price(segment, deal_type, deal_size_str, region, industry):
+    try:
+        deal_size = float(deal_size_str.replace("$", "").replace(",", ""))
+        arm = bandit.select_arm()
+        price = PRICE_POINTS[arm]
+        acceptance_prob = max(0.1, 1 - (price / PRICE_POINTS[-1]) * 0.8)
+        accepted = np.random.binomial(1, acceptance_prob)
+        bandit.update(arm, accepted)
+        return f"Recommended Price: ${price:,}\nClient would {'accept' if accepted else 'decline'} this price."
+    except Exception as e:
+        return str(e)
+
+with gr.Blocks() as app:
+    gr.Markdown("# Dynamic Pricing Bandit App")
+    with gr.Row():
+        with gr.Column():
+            segment_input = gr.Dropdown(choices=SEGMENTS, label="Client Segment")
+            deal_type_input = gr.Dropdown(choices=DEAL_TYPES, label="Deal Type")
+            deal_size_input = gr.Textbox(label="Deal Size (USD)", value="$50000000")
+            region_input = gr.Dropdown(choices=REGIONS, label="Region")
+            industry_input = gr.Dropdown(choices=INDUSTRIES, label="Industry")
+            btn = gr.Button("Get Recommendation")
+        with gr.Column():
+            result = gr.Markdown()
+
+    btn.click(fn=recommend_price,
+              inputs=[segment_input, deal_type_input, deal_size_input, region_input, industry_input],
+              outputs=result)
+
+if __name__ == "__main__":
+    app.launch()

requirements.txt

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+gradio
+datasets
+pandas
+numpy