Update app.py

app.py

@@ -1,43 +1,41 @@
 import gradio as gr
 import pandas as pd
-import matplotlib.pyplot as plt
 import matplotlib
 matplotlib.use("Agg")
-import seaborn as sns
 import requests
-from pathlib import Path
-from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
 
-# ─────────────────────────────────────────
-# SECTION 1 — Charger les données pré-calculées
-# ─────────────────────────────────────────
+# Charger les artifacts si présents
 try:
     df_pricing = pd.read_csv("artifacts/pricing_decisions.csv")
-    df_sales = pd.read_csv("artifacts/dashboard_data.csv")
     ARTIFACTS_OK = True
 except Exception:
-    ARTIFACTS_OK = False
     df_pricing = pd.DataFrame()
-    df_sales = pd.DataFrame()
+    ARTIFACTS_OK = False
+
 
-# ─────────────────────────────────────────
-# SECTION 2 — Analyse en temps réel (n8n)
-# ─────────────────────────────────────────
 def analyze_book(title, reviews_text, avg_units_sold):
     if not title or not reviews_text:
-        return "⚠️ Please enter a title and at least one review.", "", None
+        return "Please enter a title and at least one review.", "", None
 
     url = "https://matteoadam.app.n8n.cloud/webhook/price-decider"
 
     payload = {
         "title": title,
         "reviews": reviews_text,
-        "avg_units_sold": avg_units_sold
+        "avg_units_sold": avg_units_sold,
     }
 
     try:
-        response = requests.post(url, json=payload)
-        data = response.json()
+        response = requests.post(url, json=payload, timeout=30)
+
+        try:
+            data = response.json()
+        except Exception:
+            return (
+                f"Status code: {response.status_code}\nRaw response:\n{response.text}",
+                "Non-JSON response",
+                None,
+            )
 
         if isinstance(data, list) and len(data) > 0:
             result = data[0]
@@ -45,7 +43,7 @@ def analyze_book(title, reviews_text, avg_units_sold):
             result = data
 
         summary = (
-            f"📚 {title}\n\n"
+            f"Book: {title}\n\n"
             f"Pricing Decision: {result.get('pricing_decision', 'Error')}\n"
             f"Sentiment: {result.get('sentiment', 'N/A')}"
         )
@@ -56,64 +54,35 @@ def analyze_book(title, reviews_text, avg_units_sold):
         return f"Error: {str(e)}", "Request failed", None
 
 
-# ─────────────────────────────────────────
-# SECTION 3 — Interface Gradio
-# ─────────────────────────────────────────
-with gr.Blocks(title="📚 Book Price Decider", theme=gr.themes.Soft()) as app:
-
-    gr.Markdown("# 📚 Book Price Decider — Group A4")
-    gr.Markdown("Sentiment analysis + ARIMA-based pricing decisions for books.")
+with gr.Blocks(title="Book Price Decider") as app:
+    gr.Markdown("# Book Price Decider — Group A4")
+    gr.Markdown("Sentiment analysis + pricing recommendation")
 
     with gr.Tabs():
-
-        # 📊 Dashboard
-        with gr.Tab("📊 Dashboard"):
-            gr.Markdown("### Pre-computed results from the analysis notebooks")
-
+        with gr.Tab("Dashboard"):
             if ARTIFACTS_OK:
-                with gr.Row():
-                    gr.Image("artifacts/sales_trends.png", label="Sales Trends")
-                    gr.Image("artifacts/sentiment_distribution.png", label="Sentiment Distribution")
-
                 gr.Dataframe(value=df_pricing, label="Pricing Decisions Table")
             else:
-                gr.Markdown("⚠️ No artifacts found yet.")
-
-        # 🔮 Analyse
-        with gr.Tab("🔮 Analyze a New Book"):
-            gr.Markdown("### Enter book info to get a live pricing recommendation")
-
-            with gr.Row():
-                title_input = gr.Textbox(label="Book Title")
-                units_input = gr.Number(label="Avg Monthly Units Sold", value=100)
+                gr.Markdown("No artifacts found yet.")
 
+        with gr.Tab("Analyze a New Book"):
+            title_input = gr.Textbox(label="Book Title")
+            units_input = gr.Number(label="Avg Monthly Units Sold", value=100)
             reviews_input = gr.Textbox(label="Reviews (one per line)", lines=6)
 
-            analyze_btn = gr.Button("🚀 Analyze & Decide")
-
-            with gr.Row():
-                summary_output = gr.Textbox(label="Summary")
-                details_output = gr.Textbox(label="Details")
+            analyze_btn = gr.Button("Analyze")
 
-            chart_output = gr.Plot()
+            summary_output = gr.Textbox(label="Summary", lines=6)
+            details_output = gr.Textbox(label="Details", lines=2)
+            chart_output = gr.Plot(label="Chart")
 
             analyze_btn.click(
                 fn=analyze_book,
                 inputs=[title_input, reviews_input, units_input],
-                outputs=[summary_output, details_output, chart_output]
+                outputs=[summary_output, details_output, chart_output],
             )
 
-        # ℹ️ About
-        with gr.Tab("ℹ️ About"):
-            gr.Markdown("""
-## About this app
-
-AI project using:
-- Data scraping
-- Synthetic data
-- Sentiment analysis
-- ARIMA forecasting
-- Pricing decision system
-""")
+        with gr.Tab("About"):
+            gr.Markdown("AI for Big Data Management project app.")
 
 app.launch()