Update app.py

app.py

@@ -13,16 +13,16 @@ from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
 # SECTION 1 — Charger les données pré-calculées
 # ─────────────────────────────────────────
 try:
-    df_pricing   = pd.read_csv("artifacts/pricing_decisions.csv")
-    df_sales     = pd.read_csv("artifacts/dashboard_data.csv")
+    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()
+    df_pricing = pd.DataFrame()
+    df_sales = pd.DataFrame()
 
 # ─────────────────────────────────────────
-# SECTION 2 — Analyse en temps réel (VADER)
+# SECTION 2 — Analyse en temps réel
 # ─────────────────────────────────────────
 analyzer = SentimentIntensityAnalyzer()
 
@@ -65,101 +65,4 @@ Status code: {response.status_code}
 
 Raw response:
 ```text
-{response.text}
-
-# ─────────────────────────────────────────
-# 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.Tabs():
-
-        # ── Tab 1 : Dashboard pré-calculé ──────────────────
-        with gr.Tab("📊 Dashboard"):
-            gr.Markdown("### Pre-computed results from the analysis notebooks")
-
-            if ARTIFACTS_OK:
-                with gr.Row():
-                    gr.Image(value="artifacts/sales_trends.png",
-                             label="Sales Trends")
-                    gr.Image(value="artifacts/sentiment_distribution.png",
-                             label="Sentiment Distribution")
-                gr.Dataframe(value=df_pricing, label="Pricing Decisions Table")
-            else:
-                gr.Markdown(
-                    "⚠️ No artifacts found yet. "
-                    "Run the notebooks and upload the `artifacts/` folder."
-                )
-
-        # ── Tab 2 : Analyse en temps réel ──────────────────
-        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", placeholder="e.g. The Great Gatsby")
-                units_input = gr.Number(label="Avg Monthly Units Sold", value=100)
-
-            reviews_input = gr.Textbox(
-                label="Paste reviews here (one per line)",
-                lines=6,
-                placeholder="This book was amazing!\nNot what I expected.\nDecent read overall."
-            )
-
-            analyze_btn = gr.Button("🚀 Analyze & Decide", variant="primary")
-
-            with gr.Row():
-                summary_output  = gr.Markdown(label="Summary")
-                details_output  = gr.Textbox(label="Review-by-review labels", lines=6)
-
-            chart_output = gr.Plot(label="Sentiment Chart")
-
-            analyze_btn.click(
-                fn=analyze_book,
-                inputs=[title_input, reviews_input, units_input],
-                outputs=[summary_output, details_output, chart_output]
-            )
-
-        # ── Tab 3 : À propos ───────────────────────────────
-        with gr.Tab("ℹ️ About"):
-    gr.Markdown("""
-## About this app
-
-This app is part of the **AI for Big Data Management** group project at ESCP Business School.
-
-### Pipeline
-1. **Real-world data** scraped from Books to Scrape
-2. **Synthetic data** generated to enrich with reviews & sales history
-3. **VADER sentiment analysis** on customer reviews
-4. **ARIMA forecasting** on sales time series
-5. **Rule-based pricing decisions** combining sentiment + sales volume
-6. **This Hugging Face app** as the final automation layer
-
-### Team — Group A4
-- Project Manager
-- Data Analyst
-- UX Designer(s)
-- Content Specialist
-""")
-## About this app
-
-This app is part of the **AI for Big Data Management** group project at ESCP Business School.
-
-### Pipeline
-1. **Real-world data** scraped from Books to Scrape
-2. **Synthetic data** generated to enrich with reviews & sales history
-3. **VADER sentiment analysis** on customer reviews
-4. **ARIMA forecasting** on sales time series
-5. **Rule-based pricing decisions** combining sentiment + sales volume
-6. **This Hugging Face app** as the final automation layer
-
-### Team — Group A4
-- Project Manager
-- Data Analyst
-- UX Designer(s)
-- Content Specialist
-""")
-
-app.launch()
+{response.text}