Upload 5 files

.gitignore

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+# -------------------------
+# Python
+# -------------------------
+__pycache__/
+*.py[cod]
+*.pyo
+*.pyd
+*.so
+*.egg
+*.egg-info/
+dist/
+build/
+.eggs/
+*.log
+
+# -------------------------
+# Virtual environments
+# -------------------------
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# -------------------------
+# Jupyter notebooks
+# -------------------------
+.ipynb_checkpoints/
+.notebooks_cache/
+*.nbconvert.ipynb
+
+# -------------------------
+# Data files
+# -------------------------
+data/raw/*
+!data/raw/.gitkeep
+data/processed/*
+!data/processed/.gitkeep
+
+# -------------------------
+# Models
+# -------------------------
+models/*.pkl
+models/*.joblib
+!models/.gitkeep
+
+# -------------------------
+# Streamlit / FastAPI
+# -------------------------
+.streamlit/
+*.db
+*.sqlite3
+
+# -------------------------
+# OS / Editor files
+# -------------------------
+.DS_Store
+Thumbs.db
+desktop.ini
+.vscode/
+.idea/

Dockerfile

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+# Use official Python image
+FROM python:3.10-slim
+
+WORKDIR /app
+
+# Install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy app and models
+COPY app.py .
+COPY models/ ./models/
+
+EXPOSE 8000
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

README.md

@@ -1,12 +1,9 @@
----
-title: CREDIT RISK ASSESSMENT FASTAPI
-emoji: 🌖
-colorFrom: green
-colorTo: pink
-sdk: docker
-pinned: false
-license: other
-short_description: This is just a credit risk assesser using fastapi backend.
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 💳 Credit Risk Prediction API
+
+This is a FastAPI app that serves a trained Random Forest model for credit risk prediction.
+
+## 🚀 Run Locally
+
+### 1. Build Docker Image
+```bash
+docker build -t credit-risk-api .

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+import joblib
+import pandas as pd
+
+app = FastAPI()
+
+# Load artifacts
+model = joblib.load("models/balanced_model_random_forest.pkl")
+scaler = joblib.load("models/scaler.pkl")
+encoders = joblib.load("models/encoders_dict.pkl")
+
+class LoanApplication(BaseModel):
+    person_age: float
+    person_gender: str
+    person_education: str
+    person_income: float
+    person_emp_exp: int
+    person_home_ownership: str
+    loan_amnt: float
+    loan_intent: str
+    loan_int_rate: float
+    loan_percent_income: float
+    cb_person_cred_hist_length: float
+    credit_score: int
+    previous_loan_defaults_on_file: str
+    debt_to_income: float
+    age_group: str
+
+@app.get("/")
+def root():
+    return {"message": "Credit Risk API is running inside Docker!"}
+
+@app.post("/predict")
+def predict(application: LoanApplication):
+    df = pd.DataFrame([application.dict()])
+
+    # Apply encoders
+    for col, encoder in encoders.items():
+        df[col] = encoder.transform(df[col].astype(str))
+
+    # Apply scaler
+    scaling_cols = ["person_age","person_income","loan_amnt","credit_score","loan_int_rate"]
+    df[scaling_cols] = scaler.transform(df[scaling_cols])
+
+    prediction = model.predict(df)[0]
+    return {"loan_status": int(prediction)}

requirements.txt

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+fastapi
+uvicorn
+pandas
+scikit-learn
+joblib
+xgboost
+imbalanced-learn
+pydantic