Update README.md

README.md

@@ -30,14 +30,18 @@ Both outputs ≥ 80% when Distillate_To_Feed_Ratio is between 0.20 and 0.44.
 ## Usage
 ```python
 import joblib
+import numpy as np
 from huggingface_hub import hf_hub_download
 
-path = hf_hub_download("your-username/pyrolysis-distillation-predictor", 
-                        "pyrolysis_model.joblib")
-model = joblib.load(path)
-
-prediction = model.predict([[0.35, 10, 2.5, 150, 1000]])
-print(f"NAPTHA: {prediction[0][0]:.3f}, DIESEL: {prediction[0][1]:.3f}")
-```
-
-One thing to keep in mind: HuggingFace doesn't natively "understand" sklearn models the way it understands transformers, so there's no automatic inference API. If you want a live demo, you'd pair it with a **Gradio Space** — which is actually very easy and looks great for a senior project presentation. Want me to write that too?
+model_path = hf_hub_download(
+    repo_id="lastcode/pyrolysis-distillation-predictor",
+    filename="pyrolysis_model.joblib"
+)
+model = joblib.load(model_path)
+
+# [Distillate_To_Feed_Ratio, Feed_Stage, top_stage_pressure, Temp, Feed_Flow_Rate]
+X = np.array([[0.35, 10, 2.5, 150, 1000]])
+pred = model.predict(X)
+print(f"NAPTHA: {pred[0][0]:.3f}")
+print(f"DIESEL: {pred[0][1]:.3f}")
+```