app.py

import pandas as pd
import numpy as np
import gradio as gr

# ---------- Load data ----------
df_reviews = pd.read_csv("synthetic_book_reviews.csv")
df_sales = pd.read_csv("synthetic_sales_data.csv")

# ---------- Build decision table ----------
avg_sales = df_sales.groupby("title")["units_sold"].mean().reset_index()
avg_sales.columns = ["title", "avg_units_sold"]

sentiment_counts = df_reviews.groupby(["title", "sentiment_label"]).size().unstack(fill_value=0)
sentiment_counts["total"] = sentiment_counts.sum(axis=1)

# 避免没有某类情绪时报错
for col in ["positive", "negative", "neutral"]:
    if col not in sentiment_counts.columns:
        sentiment_counts[col] = 0

sentiment_counts["positive_ratio"] = sentiment_counts["positive"] / sentiment_counts["total"].replace(0, np.nan)
sentiment_counts["negative_ratio"] = sentiment_counts["negative"] / sentiment_counts["total"].replace(0, np.nan)

df_decision = avg_sales.merge(sentiment_counts.reset_index(), on="title", how="left")

def pricing_decision(row):
    if row["avg_units_sold"] >= 120 and row["positive_ratio"] >= 0.6:
        return "increase price"
    elif row["avg_units_sold"] <= 60 and row["negative_ratio"] >= 0.4:
        return "decrease price"
    else:
        return "keep price"

df_decision["pricing_decision"] = df_decision.apply(pricing_decision, axis=1)

# ---------- Gradio UI function ----------
titles = sorted(df_decision["title"].dropna().unique().tolist())

def query_book(title):
    row = df_decision[df_decision["title"] == title].iloc[0]
    return {
        "title": title,
        "avg_units_sold": float(row["avg_units_sold"]),
        "positive_ratio": float(row["positive_ratio"]) if pd.notna(row["positive_ratio"]) else None,
        "negative_ratio": float(row["negative_ratio"]) if pd.notna(row["negative_ratio"]) else None,
        "pricing_decision": row["pricing_decision"]
    }

demo = gr.Interface(
    fn=query_book,
    inputs=gr.Dropdown(choices=titles, label="Select a book title"),
    outputs=gr.JSON(label="Pricing Decision Result"),
    title="Book Pricing Decision Demo",
    description="Rule-based pricing decision based on average sales and sentiment ratios."
)

if __name__ == "__main__":
    demo.launch()