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Dockerfile

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+# Use a lightweight and secure Python base image
+FROM python:3.10-slim
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Create a non-root user for better security
+RUN addgroup --system app && adduser --system --group app
+
+# Copy the requirements file and install dependencies
+COPY --chown=app:app ./requirements.txt /app/requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /app/requirements.txt
+
+# Copy the rest of your application code
+COPY --chown=app:app . /app
+
+# Switch to the non-root user
+USER app
+
+# Expose the standard port for Hugging Face Spaces
+EXPOSE 7860
+
+# Command to run the application using the standard HF port
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+# backend/app.py
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import joblib
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import os
+
+app = FastAPI(title="PulmoProbe AI API")
+
+# Add CORS middleware to allow your Vercel app to call the API
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# --- Download and Load Model from Hugging Face Hub ---
+# This points to your model repository
+MODEL_REPO_ID = "costaspinto/PulmoProbe" 
+MODEL_FILENAME = "best_model.joblib"
+
+print("Downloading model from Hugging Face Hub...")
+model_path = hf_hub_download(repo_id=MODEL_REPO_ID, filename=MODEL_FILENAME)
+model = joblib.load(model_path)
+print("Model loaded successfully.")
+
+# --- Define Input Data Model ---
+class PatientData(BaseModel):
+    age: float
+    gender: str
+    country: str
+    cancer_stage: str
+    family_history: int
+    smoking_status: str
+    bmi: float
+    cholesterol_level: float
+    hypertension: int
+    asthma: int
+    cirrhosis: int
+    other_cancer: int
+    treatment_type: str
+
+# --- Define API Endpoints ---
+@app.get("/")
+def read_root():
+    return {"message": "Welcome to the PulmoProbe AI API"}
+
+@app.post("/predict")
+def predict(data: PatientData):
+    try:
+        input_df = pd.DataFrame([data.dict()])
+        probabilities = model.predict_proba(input_df)[0]
+        confidence_high_risk = probabilities[0]
+        risk_level = "High Risk of Non-Survival" if confidence_high_risk > 0.5 else "Low Risk of Non-Survival"
+
+        return {
+            "risk": risk_level,
+            "confidence": f"{confidence_high_risk * 100:.1f}"
+        }
+    except Exception as e:
+        return {"error": str(e)}

requirements.txt

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+fastapi
+uvicorn[standard]
+scikit-learn
+pandas
+joblib
+huggingface_hub