app.py

import gradio as gr
import pandas as pd
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
import requests

# Load results
df = pd.read_csv('final_results.csv')
analyzer = SentimentIntensityAnalyzer()

# Your n8n webhook URL
N8N_WEBHOOK_URL = "https://andreeaalbulescu.app.n8n.cloud/webhook-test/book-price-optimizer"

def analyze_book(book_title):
    match = df[df['title'].str.contains(book_title, case=False, na=False)]
    
    if len(match) > 0:
        row = match.iloc[0]
        sentiment = row['avg_sentiment']
        recommendation = row['recommendation']
        current_price = row['price']
        recommended_price = row['recommended_price']
        rating = row['rating']
        
        if sentiment > 0.2:
            sentiment_label = "Positive 😊"
        elif sentiment < -0.1:
            sentiment_label = "Negative 😞"
        else:
            sentiment_label = "Neutral 😐"
        
        result = f"""
📚 BOOK ANALYSIS RESULTS
========================
Title: {row['title']}
Current Price: £{current_price}
Rating: {'⭐' * int(rating)}

📊 SENTIMENT ANALYSIS
Sentiment: {sentiment_label}
Score: {round(sentiment, 3)}

💰 PRICING RECOMMENDATION
Action: {recommendation}
Recommended Price: £{recommended_price}
        """
        
        # Send to n8n webhook
        try:
            requests.post(N8N_WEBHOOK_URL, json={
                "book": book_title,
                "sentiment": round(float(sentiment), 3),
                "recommendation": recommendation,
                "current_price": float(current_price),
                "recommended_price": float(recommended_price)
            }, timeout=5)
        except:
            pass
            
    else:
        score = analyzer.polarity_scores(book_title)['compound']
        if score > 0.2:
            result = f"Book not in database.\nSentiment: Positive (score: {round(score,3)})\nSuggestion: Consider maintaining or increasing price."
        else:
            result = f"Book not in database.\nSentiment: Neutral/Negative (score: {round(score,3)})\nSuggestion: Consider reviewing your pricing."
        
        # Send to n8n webhook
        try:
            requests.post(N8N_WEBHOOK_URL, json={
                "book": book_title,
                "sentiment": round(score, 3),
                "recommendation": "UNKNOWN - not in database",
                "current_price": None,
                "recommended_price": None
            }, timeout=5)
        except:
            pass
    
    return result

iface = gr.Interface(
    fn=analyze_book,
    inputs=gr.Textbox(
        label="Enter Book Title",
        placeholder="e.g. A Light in the Attic"
    ),
    outputs=gr.Textbox(label="Analysis Results"),
    title="📚 Book Price Optimizer",
    description="Enter a book title to get sentiment analysis and AI-powered pricing recommendations. Results are automatically sent to our n8n automation pipeline.",
    examples=[
        ["A Light in the Attic"],
        ["Tipping the Velvet"],
        ["Sharp Objects"]
    ]
)

iface.launch()