Upload 4 files

Dockerfile

@@ -1,4 +1,3 @@
-
 FROM python:3.11-slim
 
 ENV PYTHONDONTWRITEBYTECODE=1
@@ -10,7 +9,23 @@ WORKDIR /app
 
 COPY requirements.txt /app/requirements.txt
 
-RUN pip install --no-cache-dir --upgrade pip &&     pip install --no-cache-dir -r requirements.txt
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    curl \
+    git \
+    r-base \
+    r-base-dev \
+    libcurl4-openssl-dev \
+    libssl-dev \
+    libxml2-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir -r requirements.txt && \
+    python -m ipykernel install --sys-prefix --name python3 --display-name "Python 3"
+
+RUN Rscript -e "install.packages(c('IRkernel','ggplot2','dplyr','readr','forecast','tseries'), repos='https://cloud.r-project.org')" && \
+    Rscript -e "IRkernel::installspec(user = FALSE)"
 
 COPY . /app
 

app.py

@@ -1,12 +1,14 @@
 import os
 import time
 import traceback
+import subprocess
 from pathlib import Path
 
 import gradio as gr
 import pandas as pd
 import joblib
 import papermill as pm
+from jupyter_client.kernelspec import KernelSpecManager
 
 BASE_DIR = Path(__file__).resolve().parent
 
@@ -20,7 +22,7 @@ PIPELINE_CANDIDATES = [
 ]
 
 RUNS_DIR = BASE_DIR / "runs"
-PAPERMILL_TIMEOUT = 1800
+PAPERMILL_TIMEOUT = int(os.environ.get("PAPERMILL_TIMEOUT", "1800"))
 
 
 def ensure_dirs():
@@ -31,16 +33,42 @@ def stamp():
     return time.strftime("%Y%m%d-%H%M%S")
 
 
-def run_notebook(nb_path: Path, label: str) -> str:
+def available_kernels():
+    try:
+        return KernelSpecManager().find_kernel_specs()
+    except Exception:
+        return {}
+
+
+def python_kernel_name():
+    kernels = available_kernels()
+    for name in ["python3", "python", "python-3"]:
+        if name in kernels:
+            return name
+    return None
+
+
+def r_kernel_name():
+    kernels = available_kernels()
+    for name in ["ir", "irkernel", "r"]:
+        if name in kernels:
+            return name
+    return None
+
+
+def run_notebook(nb_path: Path, label: str, kernel_name: str | None) -> str:
     ensure_dirs()
     if not nb_path.exists():
         return f"❌ {label} not found: {nb_path.name}"
+    if not kernel_name:
+        return f"❌ {label} kernel is not available."
     try:
-        out_path = RUNS_DIR / f"{stamp()}_{nb_path.name}"
+        out_path = RUNS_DIR / f"run_{stamp()}_{nb_path.name}"
         pm.execute_notebook(
             input_path=str(nb_path),
             output_path=str(out_path),
             cwd=str(BASE_DIR),
+            kernel_name=kernel_name,
             log_output=True,
             progress_bar=False,
             request_save_on_cell_execute=True,
@@ -52,11 +80,11 @@ def run_notebook(nb_path: Path, label: str) -> str:
 
 
 def run_python():
-    return run_notebook(PY_NOTEBOOK, "Python notebook")
+    return run_notebook(PY_NOTEBOOK, "Python notebook", python_kernel_name())
 
 
 def run_r():
-    return run_notebook(R_NOTEBOOK, "R notebook")
+    return run_notebook(R_NOTEBOOK, "R notebook", r_kernel_name())
 
 
 def run_pipeline():
@@ -68,7 +96,6 @@ def run_pipeline():
     if target is None:
         return "❌ pipeline.py not found."
     try:
-        import subprocess
         proc = subprocess.run(
             ["python", str(target)],
             cwd=str(BASE_DIR),
@@ -85,17 +112,17 @@ def run_pipeline():
 
 
 def run_all():
-    py_log = run_python()
-    r_log = run_r()
-    pipe_log = run_pipeline()
-    return "\n\n".join([
+    parts = [
         "=== Run Python ===",
-        py_log,
+        run_python(),
+        "",
         "=== Run R ===",
-        r_log,
+        run_r(),
+        "",
         "=== Run Pipeline ===",
-        pipe_log,
-    ])
+        run_pipeline(),
+    ]
+    return "\n".join(parts)
 
 
 def load_model():
@@ -143,48 +170,65 @@ def dashboard_data():
 
     age_df = pd.DataFrame({"AgeBand": [], "churn_rate": []})
     if {"Age", "Exited"}.issubset(df.columns):
-        temp = df.copy()
-        temp["AgeBand"] = pd.cut(
-            temp["Age"],
+        tmp = df.copy()
+        tmp["AgeBand"] = pd.cut(
+            tmp["Age"],
             bins=[18, 30, 40, 50, 60, 70],
             include_lowest=True
         )
-        age_df = temp.groupby("AgeBand").agg(churn_rate=("Exited", "mean")).reset_index()
+        age_df = tmp.groupby("AgeBand").agg(churn_rate=("Exited", "mean")).reset_index()
         age_df["AgeBand"] = age_df["AgeBand"].astype(str)
         age_df["churn_rate"] = (age_df["churn_rate"] * 100).round(2)
 
+    kernel_info = ", ".join(sorted(available_kernels().keys())) or "none"
+    summary += f"\n- Available Kernels: **{kernel_info}**"
+
     return summary, geo_df, age_df, df
 
 
 def build_ui():
-    css = (BASE_DIR / "style.css").read_text(encoding="utf-8")
+    css_path = BASE_DIR / "style.css"
+    css = css_path.read_text(encoding="utf-8") if css_path.exists() else ""
 
     with gr.Blocks(title="Bank Churn Dashboard") as demo:
         gr.HTML(f"<style>{css}</style>")
 
-        gr.Markdown("# 🏦 Bank Churn Dashboard")
+        gr.Markdown(
+            "# 🏦 Bank Churn Dashboard\n"
+            "*Run the Python and R analyses first, then review the dashboard and predictions.*",
+            elem_id="escp_title",
+        )
 
         with gr.Tab("Run Analyses"):
-            gr.Markdown("Run Python analysis, R analysis, or all analyses before reviewing the dashboard.")
             with gr.Row():
-                btn_py = gr.Button("Run Python")
-                btn_r = gr.Button("Run R")
-                btn_all = gr.Button("Run All")
-            out_py = gr.Textbox(lines=14, label="Python Log")
-            out_r = gr.Textbox(lines=14, label="R Log")
-            out_all = gr.Textbox(lines=18, label="Run All Log")
+                btn_py = gr.Button("Run Python", variant="secondary")
+                btn_r = gr.Button("Run R", variant="secondary")
+                btn_all = gr.Button("Run All", variant="primary")
+
+            py_log = gr.Textbox(label="Python Log", lines=12, max_lines=18, interactive=False)
+            r_log = gr.Textbox(label="R Log", lines=12, max_lines=18, interactive=False)
+            all_log = gr.Textbox(label="Run All Log", lines=18, max_lines=28, interactive=False)
 
-            btn_py.click(run_python, outputs=out_py)
-            btn_r.click(run_r, outputs=out_r)
-            btn_all.click(run_all, outputs=out_all)
+            btn_py.click(run_python, outputs=[py_log])
+            btn_r.click(run_r, outputs=[r_log])
+            btn_all.click(run_all, outputs=[all_log])
 
         with gr.Tab("Dashboard"):
-            refresh_btn = gr.Button("Refresh Dashboard")
+            refresh_btn = gr.Button("Refresh Dashboard", variant="primary")
             summary_md = gr.Markdown()
             with gr.Row():
-                geo_plot = gr.BarPlot(x="Geography", y="Exited", title="Churn by Geography (%)")
-                age_plot = gr.LinePlot(x="AgeBand", y="churn_rate", title="Churn by Age Band (%)")
-            data_table = gr.Dataframe(interactive=True, label="Customer Data")
+                geo_plot = gr.BarPlot(
+                    x="Geography",
+                    y="Exited",
+                    title="Churn by Geography (%)",
+                    vertical=False
+                )
+                age_plot = gr.LinePlot(
+                    x="AgeBand",
+                    y="churn_rate",
+                    title="Churn by Age Band (%)"
+                )
+            data_table = gr.Dataframe(label="Customer Data", interactive=True)
 
             refresh_btn.click(
                 dashboard_data,
@@ -196,11 +240,12 @@ def build_ui():
             )
 
         with gr.Tab("Prediction"):
-            age = gr.Number(label="Age", value=35)
-            balance = gr.Number(label="Balance", value=5000)
-            pred_btn = gr.Button("Predict")
+            with gr.Row():
+                age = gr.Number(label="Age", value=35)
+                balance = gr.Number(label="Balance", value=5000)
+            pred_btn = gr.Button("Predict", variant="primary")
             pred_out = gr.Textbox(label="Prediction Result")
-            pred_btn.click(predict, inputs=[age, balance], outputs=pred_out)
+            pred_btn.click(predict, inputs=[age, balance], outputs=[pred_out])
 
     return demo
 

requirements.txt

@@ -1,18 +1,20 @@
-gradio>=4.0
-pandas
-numpy
-matplotlib
-seaborn
-statsmodels
-scikit-learn
-scipy
-joblib
-papermill
-nbformat
-pillow
-requests
-beautifulsoup4
-vaderSentiment
-huggingface_hub
-textblob
-faker
+gradio==6.0.0
+pandas>=2.0.0
+numpy>=1.24.0
+matplotlib>=3.7.0
+seaborn>=0.13.0
+statsmodels>=0.14.0
+scikit-learn>=1.3.0
+scipy>=1.10.0
+joblib>=1.3.0
+papermill>=2.5.0
+nbformat>=5.9.0
+ipykernel>=6.29.0
+jupyter_client>=8.6.0
+pillow>=10.0.0
+requests>=2.31.0
+beautifulsoup4>=4.12.0
+vaderSentiment>=3.3.2
+huggingface_hub>=0.20.0
+textblob>=0.18.0
+faker>=20.0.0

style.css

@@ -1,4 +1,4 @@
-/* Background logic restored from the first app style */
+/* Background restored to first-app style logic */
 gradio-app,
 .gradio-app,
 .main,
@@ -23,32 +23,37 @@ html, body {
   min-height: 100vh !important;
 }
 
-/* Restore the top banner spacing like the first app */
+/* Top spacing like the first app */
 .gradio-container {
   max-width: 1400px !important;
   width: 94vw !important;
   margin: 0 auto !important;
   padding-top: 220px !important;
-  padding-bottom: 120px !important;
+  padding-bottom: 150px !important;
   background: transparent !important;
 }
 
 /* Title */
-h1 {
-  color: #ffffff !important;
+#escp_title h1 {
+  color: rgb(242, 198, 55) !important;
   font-size: 3rem !important;
   font-weight: 800 !important;
   text-align: center !important;
   margin: 0 0 12px 0 !important;
-  text-shadow: 0 2px 10px rgba(0, 0, 0, 0.35) !important;
 }
 
-/* Tab bar */
+#escp_title p,
+#escp_title em {
+  color: rgba(255, 255, 255, 0.90) !important;
+  text-align: center !important;
+}
+
+/* Tabs */
 .tabs > .tab-nav,
 .tab-nav,
 div[role="tablist"],
 .svelte-tabs > .tab-nav {
-  background: rgba(15, 23, 42, 0.58) !important;
+  background: rgba(15, 23, 42, 0.60) !important;
   border-radius: 10px 10px 0 0 !important;
   padding: 4px !important;
 }
@@ -79,29 +84,38 @@ div[role="tablist"] button[aria-selected="true"],
   background: rgba(255, 255, 255, 0.12) !important;
 }
 
-/* Panels */
+.tabs > .tab-nav button:not(.selected),
+.tab-nav button:not(.selected),
+button[role="tab"][aria-selected="false"],
+button[role="tab"]:not(.selected),
+div[role="tablist"] button:not([aria-selected="true"]) {
+  color: #ffffff !important;
+  opacity: 1 !important;
+}
+
+/* White cards */
 .gradio-container .gr-block,
 .gradio-container .gr-box,
 .gradio-container .gr-panel,
-.gradio-container .gr-group,
-.tabitem {
-  background: rgba(255, 255, 255, 0.96) !important;
-  border-radius: 12px !important;
+.gradio-container .gr-group {
+  background: #ffffff !important;
+  border-radius: 10px !important;
 }
 
 .tabitem {
+  background: rgba(255, 255, 255, 0.96) !important;
+  border-radius: 0 0 10px 10px !important;
   padding: 16px !important;
 }
 
-/* Inputs and buttons */
-input,
-textarea,
-select {
-  background: #ffffff !important;
-  color: #111827 !important;
+/* Inputs */
+.gradio-container input,
+.gradio-container textarea,
+.gradio-container select {
   border-radius: 10px !important;
 }
 
+/* Buttons */
 button:not([role="tab"]) {
   border-radius: 10px !important;
   font-weight: 700 !important;