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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/Bank[[:space:]]Churn.png filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,19 @@
+
+FROM python:3.11-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+ENV GRADIO_SERVER_NAME=0.0.0.0
+ENV GRADIO_SERVER_PORT=7860
+
+WORKDIR /app
+
+COPY requirements.txt /app/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade pip &&     pip install --no-cache-dir -r requirements.txt
+
+COPY . /app
+
+EXPOSE 7860
+
+CMD ["python","-u","app.py"]

README.md

@@ -1,10 +1,23 @@
+
 ---
-title: Bc4
-emoji: 🔥
-colorFrom: purple
-colorTo: indigo
+title: Bank Churn Predictor
+emoji: 🏦
+colorFrom: blue
+colorTo: yellow
 sdk: docker
-pinned: false
+app_port: 7860
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Bank Churn Predictor
+
+A stable HuggingFace Docker Space demo.
+
+Features
+- Run Pipeline (train model)
+- Execution log viewer
+- Single prediction UI
+- Background image support
+
+The pipeline trains a simple model and saves:
+
+models/pipeline.joblib

app.py

@@ -0,0 +1,67 @@
+
+import os
+import subprocess
+import gradio as gr
+import pandas as pd
+import joblib
+
+MODEL_PATH="models/pipeline.joblib"
+
+def load_model():
+    if os.path.exists(MODEL_PATH):
+        return joblib.load(MODEL_PATH)
+    return None
+
+model=load_model()
+
+def predict(age,balance):
+    global model
+    model=load_model()
+    if model is None:
+        return "Run pipeline first"
+    df=pd.DataFrame([[age,balance]],columns=["Age","Balance"])
+    p=model.predict(df)[0]
+    return f"Prediction: {p}"
+
+def run_pipeline():
+    proc=subprocess.Popen(
+        ["python","scripts/pipeline.py"],
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        text=True
+    )
+    log=""
+    for line in proc.stdout:
+        log+=line
+        yield log
+
+def build_ui():
+    css=open("style.css").read()
+
+    with gr.Blocks() as demo:
+
+        gr.HTML(f"<style>{css}</style>")
+
+        gr.Markdown("# 🏦 Bank Churn Predictor")
+
+        with gr.Tab("Pipeline"):
+            gr.Markdown("Train model and view execution log")
+            btn=gr.Button("Run Pipeline")
+            log=gr.Textbox(lines=20,label="Execution Log")
+            btn.click(run_pipeline,outputs=log)
+
+        with gr.Tab("Prediction"):
+            age=gr.Number(label="Age")
+            balance=gr.Number(label="Balance")
+            btn=gr.Button("Predict")
+            out=gr.Textbox()
+
+            btn.click(predict,[age,balance],out)
+
+    return demo
+
+if __name__=="__main__":
+    demo=build_ui()
+    demo.queue()
+    port=int(os.environ.get("PORT",7860))
+    demo.launch(server_name="0.0.0.0",server_port=port)

requirements.txt

@@ -0,0 +1,6 @@
+gradio>=4.0
+pandas
+numpy
+scikit-learn
+joblib
+matplotlib

scripts/pipeline.py

@@ -0,0 +1,31 @@
+
+import pandas as pd
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+import joblib, os
+
+print("===== STEP 1: Load Data =====")
+
+data = pd.DataFrame({
+"Age":[25,45,33,52,23,40,60,48],
+"Balance":[1000,5000,2300,8000,1200,4500,9000,3000],
+"Exited":[0,1,0,1,0,1,1,0]
+})
+
+print("Dataset ready")
+
+print("===== STEP 2: Train Model =====")
+
+X=data[["Age","Balance"]]
+y=data["Exited"]
+
+X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)
+
+model=LogisticRegression()
+model.fit(X_train,y_train)
+
+os.makedirs("models",exist_ok=True)
+joblib.dump(model,"models/pipeline.joblib")
+
+print("Model saved to models/pipeline.joblib")
+print("Pipeline finished")

style.css

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+
+body {
+background:#0f172a;
+color:white;
+}
+
+.gradio-container {
+background:transparent;
+}
+