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app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+import io
+import re
+from datetime import datetime
+
+# ── VADER sentiment (graceful fallback if not installed) ──────────────────────
+try:
+    from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+    _analyzer = SentimentIntensityAnalyzer()
+    def vader_score(text):
+        return _analyzer.polarity_scores(str(text))
+except ImportError:
+    def vader_score(text):
+        text = text.lower()
+        pos = sum(w in text for w in ["good","strong","growth","positive","gain","profit"])
+        neg = sum(w in text for w in ["breach","hack","lawsuit","strike","loss","fine","shut","miss","fraud","attack"])
+        compound = round((pos - neg) / max(pos + neg, 1), 3)
+        return {"neg": neg / max(pos+neg,1), "neu": 0.5, "pos": pos / max(pos+neg,1), "compound": compound}
+
+# ── Styling constants ─────────────────────────────────────────────────────────
+PALETTE = {
+    "critical response": "#C0392B",
+    "escalate":          "#E67E22",
+    "review":            "#2980B9",
+    "monitor":           "#27AE60",
+}
+BG      = "#0D0F14"
+CARD_BG = "#141720"
+ACCENT  = "#4F8EF7"
+TEXT    = "#E8EAF0"
+
+CRISIS_ICONS = {
+    "cybersecurity": "🔐",
+    "legal":         "⚖️",
+    "operations":    "🏭",
+    "labor":         "👷",
+    "financial":     "📉",
+}
+
+# ─────────────────────────────────────────────────────────────────────────────
+# HELPERS
+# ─────────────────────────────────────────────────────────────────────────────
+
+def infer_crisis_type(text):
+    text = text.lower()
+    if any(w in text for w in ["hack","breach","cyber","ransomware","malware","data leak","phishing"]):
+        return "cybersecurity"
+    if any(w in text for w in ["lawsuit","antitrust","regulator","fine","court","SEC","penalty","sanction"]):
+        return "legal"
+    if any(w in text for w in ["factory","supply chain","shutdown","production","recall","outage"]):
+        return "operations"
+    if any(w in text for w in ["strike","worker","protest","union","layoff","walkout"]):
+        return "labor"
+    if any(w in text for w in ["profit warning","earnings miss","downgrade","revenue","loss","debt","bankruptcy"]):
+        return "financial"
+    return "general"
+
+SEVERITY_BASE = {
+    "cybersecurity": 4, "legal": 4, "operations": 3,
+    "labor": 3, "financial": 5, "general": 3,
+}
+URGENCY_BASE = {
+    "cybersecurity": 5, "legal": 4, "operations": 4,
+    "labor": 3, "financial": 5, "general": 3,
+}
+MARKET_BASE = {
+    "cybersecurity": -2.8, "legal": -2.1, "operations": -1.8,
+    "labor": -1.2, "financial": -3.0, "general": -1.5,
+}
+
+def assign_priority(severity, urgency):
+    score = severity + urgency
+    if score >= 9:  return "critical response"
+    if score >= 7:  return "escalate"
+    if score >= 5:  return "review"
+    return "monitor"
+
+def sentiment_label(compound):
+    if compound >=  0.05: return "Positive 🟢"
+    if compound <= -0.05: return "Negative 🔴"
+    return "Neutral ⚪"
+
+# ─────────────────────────────────────────────────────────────────────────────
+# TAB 1 — Single headline analyser
+# ─────────────────────────────────────────────────────────────────────────────
+
+def analyse_headline(headline: str):
+    if not headline.strip():
+        return "⚠️ Please enter a headline.", None
+
+    scores     = vader_score(headline)
+    compound   = round(scores["compound"], 3)
+    crisis     = infer_crisis_type(headline)
+    severity   = SEVERITY_BASE[crisis]
+    urgency    = URGENCY_BASE[crisis]
+    market     = round(MARKET_BASE[crisis] + np.random.normal(0, 0.4), 2)
+    priority   = assign_priority(severity, urgency)
+    sent_label = sentiment_label(compound)
+    icon       = CRISIS_ICONS.get(crisis, "📰")
+    color      = PALETTE.get(priority, "#888")
+
+    # ── bar chart ─────────────────────────────────────────────────────────────
+    fig, axes = plt.subplots(1, 2, figsize=(9, 3.5))
+    fig.patch.set_facecolor(BG)
+
+    # sentiment bars
+    ax = axes[0]
+    ax.set_facecolor(CARD_BG)
+    cats   = ["Negative", "Neutral", "Positive"]
+    vals   = [scores["neg"], scores["neu"], scores["pos"]]
+    colors = ["#C0392B", "#7F8C8D", "#27AE60"]
+    bars   = ax.barh(cats, vals, color=colors, height=0.5)
+    ax.set_xlim(0, 1)
+    ax.set_title("Sentiment Breakdown", color=TEXT, fontsize=11, pad=8)
+    ax.tick_params(colors=TEXT, labelsize=9)
+    for spine in ax.spines.values(): spine.set_visible(False)
+    ax.xaxis.label.set_color(TEXT)
+    ax.set_xlabel("Score", color=TEXT, fontsize=8)
+    for bar, val in zip(bars, vals):
+        ax.text(val + 0.01, bar.get_y() + bar.get_height()/2,
+                f"{val:.2f}", va="center", color=TEXT, fontsize=8)
+
+    # severity / urgency gauge
+    ax2 = axes[1]
+    ax2.set_facecolor(CARD_BG)
+    metrics = ["Severity", "Urgency"]
+    mvals   = [severity, urgency]
+    mcols   = [ACCENT, color]
+    b2      = ax2.barh(metrics, mvals, color=mcols, height=0.5)
+    ax2.set_xlim(0, 5)
+    ax2.set_title("Risk Scores (out of 5)", color=TEXT, fontsize=11, pad=8)
+    ax2.tick_params(colors=TEXT, labelsize=9)
+    for spine in ax2.spines.values(): spine.set_visible(False)
+    ax2.set_xlabel("Score", color=TEXT, fontsize=8)
+    ax2.xaxis.label.set_color(TEXT)
+    for bar, val in zip(b2, mvals):
+        ax2.text(val + 0.05, bar.get_y() + bar.get_height()/2,
+                 str(val), va="center", color=TEXT, fontsize=9, fontweight="bold")
+
+    plt.tight_layout(pad=1.5)
+
+    # ── markdown result card ───────────────────────────────────────────────────
+    md = f"""
+### {icon} Analysis Result
+
+| Field | Value |
+|---|---|
+| **Crisis Type** | `{crisis.upper()}` |
+| **Sentiment** | {sent_label} (compound: `{compound}`) |
+| **Severity Score** | `{severity} / 5` |
+| **Response Urgency** | `{urgency} / 5` |
+| **Est. Market Impact** | `{market:+.2f}%` |
+| **Priority Action** | <span style='color:{color};font-weight:bold'>{priority.upper()}</span> |
+
+---
+**Recommended Response:** {"🚨 Immediate leadership escalation and cross-team crisis coordination required." if priority == "critical response" else "⚡ Escalate to risk and communications teams for coordinated response." if priority == "escalate" else "🔍 Schedule a structured review in the next reporting cycle." if priority == "review" else "👁️ Routine monitoring — flag if coverage increases."}
+"""
+    return md, fig
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# TAB 2 — CSV Dashboard
+# ─────────────────────────────────────────────────────────────────────────────
+
+def build_dashboard(file):
+    if file is None:
+        return "⚠️ Please upload **crisis_news_enriched.csv** (output of Notebook 1).", None
+
+    try:
+        df = pd.read_csv(file.name)
+    except Exception as e:
+        return f"❌ Could not read file: {e}", None
+
+    required = {"crisis_type", "priority_action", "severity_score",
+                "estimated_market_impact_pct", "company"}
+    missing  = required - set(df.columns)
+    if missing:
+        return f"❌ Missing columns: {missing}. Please upload `crisis_news_enriched.csv`.", None
+
+    # ── 4-panel figure ────────────────────────────────────────────────────────
+    fig, axes = plt.subplots(2, 2, figsize=(13, 9))
+    fig.patch.set_facecolor(BG)
+    fig.suptitle("Crisis Intelligence Dashboard", color=TEXT,
+                 fontsize=16, fontweight="bold", y=0.98)
+
+    # 1. Crisis type distribution
+    ax = axes[0, 0]
+    ax.set_facecolor(CARD_BG)
+    ct = df["crisis_type"].value_counts()
+    bar_colors = [ACCENT] * len(ct)
+    bars = ax.bar(ct.index, ct.values, color=bar_colors, width=0.6)
+    ax.set_title("Headlines by Crisis Type", color=TEXT, fontsize=11)
+    ax.tick_params(colors=TEXT, labelsize=8)
+    ax.set_xlabel("Crisis Type", color=TEXT, fontsize=9)
+    ax.set_ylabel("Count", color=TEXT, fontsize=9)
+    for spine in ax.spines.values(): spine.set_color("#2A2D3A")
+    for bar in bars:
+        ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.3,
+                str(int(bar.get_height())), ha="center", color=TEXT, fontsize=8)
+
+    # 2. Priority action distribution (donut)
+    ax2 = axes[0, 1]
+    ax2.set_facecolor(CARD_BG)
+    pa   = df["priority_action"].value_counts()
+    cols = [PALETTE.get(k, "#888") for k in pa.index]
+    wedges, texts, autotexts = ax2.pie(
+        pa.values, labels=pa.index, colors=cols,
+        autopct="%1.0f%%", startangle=140,
+        wedgeprops=dict(width=0.55),
+        textprops={"color": TEXT, "fontsize": 8}
+    )
+    for at in autotexts: at.set_color(BG); at.set_fontweight("bold")
+    ax2.set_title("Priority Action Distribution", color=TEXT, fontsize=11)
+
+    # 3. Avg market impact by crisis type
+    ax3 = axes[1, 0]
+    ax3.set_facecolor(CARD_BG)
+    mi = df.groupby("crisis_type")["estimated_market_impact_pct"].mean().sort_values()
+    bar_c = ["#C0392B" if v < -2.5 else "#E67E22" if v < -1.5 else "#2980B9" for v in mi.values]
+    bars3 = ax3.barh(mi.index, mi.values, color=bar_c, height=0.5)
+    ax3.axvline(0, color=TEXT, linewidth=0.5, alpha=0.4)
+    ax3.set_title("Avg Market Impact % by Crisis Type", color=TEXT, fontsize=11)
+    ax3.tick_params(colors=TEXT, labelsize=8)
+    ax3.set_xlabel("Est. Impact (%)", color=TEXT, fontsize=9)
+    for spine in ax3.spines.values(): spine.set_color("#2A2D3A")
+    for bar, val in zip(bars3, mi.values):
+        ax3.text(val - 0.05, bar.get_y() + bar.get_height()/2,
+                 f"{val:.2f}%", va="center", ha="right", color=TEXT, fontsize=8)
+
+    # 4. Severity heatmap (crisis type × priority)
+    ax4 = axes[1, 1]
+    ax4.set_facecolor(CARD_BG)
+    pivot = df.groupby(["crisis_type", "priority_action"])["severity_score"].mean().unstack(fill_value=0)
+    im = ax4.imshow(pivot.values, cmap="RdYlGn_r", aspect="auto", vmin=1, vmax=5)
+    ax4.set_xticks(range(len(pivot.columns)))
+    ax4.set_yticks(range(len(pivot.index)))
+    ax4.set_xticklabels(pivot.columns, color=TEXT, fontsize=7, rotation=20, ha="right")
+    ax4.set_yticklabels(pivot.index, color=TEXT, fontsize=8)
+    ax4.set_title("Avg Severity: Crisis Type × Priority", color=TEXT, fontsize=11)
+    for i in range(pivot.shape[0]):
+        for j in range(pivot.shape[1]):
+            val = pivot.values[i, j]
+            if val > 0:
+                ax4.text(j, i, f"{val:.1f}", ha="center", va="center",
+                         color="white", fontsize=8, fontweight="bold")
+    cbar = fig.colorbar(im, ax=ax4, fraction=0.03)
+    cbar.ax.tick_params(colors=TEXT, labelsize=7)
+
+    plt.tight_layout(rect=[0, 0, 1, 0.96])
+
+    # ── summary stats ─────────────────────────────────────────────────────────
+    n          = len(df)
+    n_critical = (df["priority_action"] == "critical response").sum()
+    top_type   = df["crisis_type"].value_counts().idxmax()
+    top_co     = df["company"].value_counts().idxmax()
+    avg_impact = df["estimated_market_impact_pct"].mean()
+
+    md = f"""
+### 📊 Dataset Summary
+
+| Metric | Value |
+|---|---|
+| Total headlines | `{n}` |
+| Critical response alerts | `{n_critical}` ({100*n_critical/n:.0f}%) |
+| Most common crisis type | `{top_type.upper()}` |
+| Most exposed company | `{top_co}` |
+| Avg estimated market impact | `{avg_impact:+.2f}%` |
+
+Upload `crisis_news_enriched.csv` (generated by Notebook 1) to refresh.
+"""
+    return md, fig
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# BUILD UI
+# ─────────────────────────────────────────────────────────────────────────────
+
+CUSTOM_CSS = """
+body, .gradio-container { background: #0D0F14 !important; color: #E8EAF0 !important; font-family: 'IBM Plex Mono', monospace; }
+.gr-button-primary { background: #4F8EF7 !important; border: none !important; color: #0D0F14 !important; font-weight: 700 !important; }
+.gr-button-primary:hover { background: #6FA3FA !important; }
+h1, h2, h3 { color: #E8EAF0 !important; }
+.gr-panel, .gr-box { background: #141720 !important; border-color: #2A2D3A !important; }
+textarea, input[type=text] { background: #1C1F2B !important; color: #E8EAF0 !important; border-color: #2A2D3A !important; }
+.gr-tab-nav button { color: #9AA0B4 !important; }
+.gr-tab-nav button.selected { color: #4F8EF7 !important; border-bottom: 2px solid #4F8EF7 !important; }
+"""
+
+ABOUT_MD = """
+# 🔍 Crisis Monitor — AI-Powered Business Risk Intelligence
+
+## What this app does
+This tool is the interactive front-end of an end-to-end crisis monitoring pipeline built for the **AI for Big Data Management** course at ESCP Business School.
+
+It lets you:
+- **Analyse any news headline** instantly — detecting crisis type, sentiment (VADER), severity, urgency, estimated market impact, and recommended action
+- **Upload your enriched dataset** (`crisis_news_enriched.csv`) for a full visual dashboard
+
+## How the pipeline works
+```
+Google News RSS  ──► Notebook 1 ──► Enriched CSV ──► Notebook 2 ──► Analysis
+                      (scraping,        (severity,       (VADER,
+                       synthetic         urgency,         ARIMA,
+                       enrichment)       market           decision
+                                        impact,           support)
+                                        priority)
+                                             │
+                                             ▼
+                                      This Hugging Face App
+                                      (real-time scanning +
+                                       dashboard visualisation)
+```
+
+## Crisis types monitored
+| Type | Signal keywords |
+|---|---|
+| 🔐 Cybersecurity | breach, hack, ransomware, data leak |
+| ⚖️ Legal | lawsuit, antitrust, regulator, fine |
+| 🏭 Operations | factory, supply chain, shutdown, recall |
+| 👷 Labor | strike, worker protest, union, layoff |
+| 📉 Financial | profit warning, earnings miss, downgrade |
+
+## Priority framework
+| Priority | Trigger |
+|---|---|
+| 🔴 Critical Response | Severity + Urgency ≥ 9 |
+| 🟠 Escalate | Score 7–8 |
+| 🔵 Review | Score 5–6 |
+| 🟢 Monitor | Score < 5 |
+
+---
+*Built with Python · Gradio · VADER Sentiment · Matplotlib*
+"""
+
+with gr.Blocks(css=CUSTOM_CSS, title="Crisis Monitor") as demo:
+
+    gr.Markdown("""
+# 🚨 Crisis Monitor
+### AI-Powered Business Risk Intelligence · ESCP Business School
+""")
+
+    with gr.Tabs():
+
+        # ── TAB 1 ─────────────────────────────────────────────────────────────
+        with gr.Tab("🔐 Headline Scanner"):
+            gr.Markdown("Paste any business news headline to get an instant risk assessment.")
+            with gr.Row():
+                with gr.Column(scale=2):
+                    headline_input = gr.Textbox(
+                        label="News Headline",
+                        placeholder="e.g. Apple faces major data breach exposing 50M user records...",
+                        lines=3,
+                    )
+                    scan_btn = gr.Button("⚡ Analyse Headline", variant="primary")
+                with gr.Column(scale=3):
+                    result_md  = gr.Markdown()
+                    result_fig = gr.Plot()
+
+            scan_btn.click(
+                fn=analyse_headline,
+                inputs=headline_input,
+                outputs=[result_md, result_fig],
+            )
+
+            gr.Examples(
+                examples=[
+                    ["Tesla hit with major ransomware attack, customer data leaked online"],
+                    ["Amazon faces antitrust fine from EU regulators over pricing practices"],
+                    ["Boeing factory workers go on strike, halting 737 MAX production"],
+                    ["Intel issues profit warning, shares drop 12% after earnings miss"],
+                    ["Apple supply chain disruption forces iPhone production cuts in China"],
+                ],
+                inputs=headline_input,
+                label="Try an example",
+            )
+
+        # ── TAB 2 ─────────────────────────────────────────────────────────────
+        with gr.Tab("📊 Crisis Dashboard"):
+            gr.Markdown("Upload `crisis_news_enriched.csv` (generated by Notebook 1) for a full portfolio view.")
+            with gr.Row():
+                csv_input = gr.File(label="Upload crisis_news_enriched.csv", file_types=[".csv"])
+                dash_btn  = gr.Button("📈 Generate Dashboard", variant="primary")
+            with gr.Row():
+                dash_md  = gr.Markdown()
+            with gr.Row():
+                dash_fig = gr.Plot()
+
+            dash_btn.click(
+                fn=build_dashboard,
+                inputs=csv_input,
+                outputs=[dash_md, dash_fig],
+            )
+
+        # ── TAB 3 ─────────────────────────────────────────────────────────────
+        with gr.Tab("ℹ️ About"):
+            gr.Markdown(ABOUT_MD)
+
+demo.launch()

requirements.txt

@@ -0,0 +1,5 @@
+gradio>=4.0.0
+pandas
+numpy
+matplotlib
+vaderSentiment