Update app.py

app.py

@@ -5,7 +5,6 @@ import matplotlib
 matplotlib.use("Agg")
 import seaborn as sns
 import requests
-import io
 from pathlib import Path
 from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
 
@@ -22,27 +21,8 @@ except Exception:
     df_sales = pd.DataFrame()
 
 # ─────────────────────────────────────────
-# SECTION 2 — Analyse en temps réel
+# SECTION 2 — Analyse en temps réel (n8n)
 # ─────────────────────────────────────────
-analyzer = SentimentIntensityAnalyzer()
-
-def get_sentiment_label(text):
-    score = analyzer.polarity_scores(text)["compound"]
-    if score >= 0.05:
-        return "positive"
-    elif score <= -0.05:
-        return "negative"
-    else:
-        return "neutral"
-
-def pricing_decision(avg_units, positive_ratio, negative_ratio):
-    if avg_units >= 120 and positive_ratio >= 0.6:
-        return "📈 Increase Price"
-    elif avg_units <= 60 and negative_ratio >= 0.4:
-        return "📉 Decrease Price"
-    else:
-        return "➡️ Keep Price"
-
 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
@@ -58,11 +38,76 @@ def analyze_book(title, reviews_text, avg_units_sold):
     try:
         response = requests.post(url, json=payload)
 
-        summary = f"""
-📚 **{title}**
+        summary = (
+            f"📚 {title}\n\n"
+            f"Status code: {response.status_code}\n\n"
+            f"Raw response:\n{response.text}"
+        )
+
+        return summary, "Debug mode", None
+
+    except Exception as e:
+        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.Tabs():
+
+        # 📊 Dashboard
+        with gr.Tab("📊 Dashboard"):
+            gr.Markdown("### Pre-computed results from the analysis notebooks")
+
+            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)
+
+            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")
+
+            chart_output = gr.Plot()
+
+            analyze_btn.click(
+                fn=analyze_book,
+                inputs=[title_input, reviews_input, units_input],
+                outputs=[summary_output, details_output, chart_output]
+            )
+
+        # ℹ️ About
+        with gr.Tab("ℹ️ About"):
+            gr.Markdown("""
+## About this app
 
-Status code: {response.status_code}
+AI project using:
+- Data scraping
+- Synthetic data
+- Sentiment analysis
+- ARIMA forecasting
+- Pricing decision system
+""")
 
-Raw response:
-```text
-{response.text}
+app.launch()