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app.py

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+from fastapi import FastAPI, HTTPException, Depends, status
+from fastapi.security import HTTPBasic, HTTPBasicCredentials
+from pydantic import BaseModel, Field
+import joblib
+import os
+import numpy as np
+
+# Initialize FastAPI app
+app = FastAPI(
+    title="Iris Classification API",
+    description="A REST API for predicting Iris species using a pre-trained scikit-learn model.",
+    version="1.0.0"
+)
+
+# --- Authentication Setup ---
+security = HTTPBasic()
+
+def get_current_username(credentials: HTTPBasicCredentials = Depends(security)):
+    correct_username = os.getenv("API_USERNAME")
+    correct_password = os.getenv("API_PASSWORD")
+
+    if not correct_username or not correct_password:
+        # This handles cases where secrets aren't set in HF Spaces (shouldn't happen if done correctly)
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail="API credentials not configured on the server."
+        )
+
+    if not (credentials.username == correct_username and credentials.password == correct_password):
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Incorrect username or password",
+            headers={"WWW-Authenticate": "Basic"},
+        )
+    return credentials.username
+
+# --- Model Loading ---
+model = None
+class_names = None
+
+@app.on_event("startup")
+async def load_artifacts():
+    global model, class_names
+    model_path = os.path.join("model", "iris_model.joblib")
+    class_names_path = os.path.join("model", "iris_class_names.joblib")
+
+    if not os.path.exists(model_path) or not os.path.exists(class_names_path):
+        raise RuntimeError(f"Model or class names file not found. Ensure '{model_path}' and '{class_names_path}' exist.")
+
+    model = joblib.load(model_path)
+    class_names = joblib.load(class_names_path)
+    print("Model and class names loaded successfully.")
+
+# --- Request Body Model ---
+class IrisFeatures(BaseModel):
+    sepal_length: float = Field(..., example=5.1, description="Sepal length in cm")
+    sepal_width: float = Field(..., example=3.5, description="Sepal width in cm")
+    petal_length: float = Field(..., example=1.4, description="Petal length in cm")
+    petal_width: float = Field(..., example=0.2, description="Petal width in cm")
+
+# --- API Endpoint ---
+@app.post("/predict", summary="Predict Iris Species", response_description="The predicted Iris species and probabilities.")
+async def predict_iris(
+    features: IrisFeatures,
+    current_user: str = Depends(get_current_username)
+):
+    if model is None or class_names is None:
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail="Model is not loaded yet. Please try again in a moment."
+        )
+
+    input_data = np.array([[
+        features.sepal_length,
+        features.sepal_width,
+        features.petal_length,
+        features.petal_width
+    ]])
+
+    prediction_index = model.predict(input_data)[0]
+    predicted_species = class_names[prediction_index]
+
+    probabilities = model.predict_proba(input_data)[0]
+    probabilities_dict = {name: float(prob) for name, prob in zip(class_names, probabilities)}
+
+    return {
+        "predicted_species": predicted_species,
+        "prediction_probabilities": probabilities_dict
+    }
+
+# --- Health Check Endpoint ---
+@app.get("/health", summary="Health Check", response_description="Indicates if the API is running.")
+async def health_check():
+    return {"status": "ok", "model_loaded": model is not None}
+
+# Note: The uvicorn.run part is for local execution.
+# Hugging Face Spaces will use the CMD in the Dockerfile.
+# For local testing in Colab, you'd use ngrok or colabcode (see below).