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Updated numpy version and minor error fixes

README.md

@@ -7,6 +7,7 @@ sdk: streamlit
 sdk_version: 1.57.0
 app_file: app.py
 pinned: false
+python_version: "3.12"
 ---
 
 # F1 Grid-to-Flag Predictor

app.py

@@ -91,7 +91,7 @@ CIRCUIT_DISPLAY = {
 
 @st.cache_resource
 def load_model():
-    with open(MODELS_DIR / "best_model.pkl", "rb") as f:
+    with open(MODELS_DIR / "f1_prediction_model.pkl", "rb") as f:
         model = pickle.load(f)
     with open(MODELS_DIR / "metadata.json") as f:
         metadata = json.load(f)
@@ -457,7 +457,7 @@ try:
 except FileNotFoundError:
     st.error(
         "Model or data files not found. "
-        "Make sure `models/best_model.pkl`, `models/metadata.json`, and "
+        "Make sure `models/f1_prediction_model.pkl`, `models/metadata.json`, and "
         "`data/processed/f1_features.csv` exist."
     )
     st.stop()

models/f1_prediction_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:af88a2a09463b762eeb21a7b23b167aba8073535b23d2867881fa614c5c3b7ab
+size 27851552

models/metadata.json

@@ -1,5 +1,5 @@
 {
-  "best_model_name": "GradBoost (n=200)",
+  "best_model_name": "RF (n=300)",
   "feature_columns": [
     "grid_position",
     "driver_id_enc",
@@ -48,58 +48,58 @@
   "results": {
     "RF (n=300)": {
       "val_2024": {
-        "accuracy": 0.5574,
-        "f1_weighted": 0.5473
+        "accuracy": 0.5658,
+        "f1_weighted": 0.5517
       },
       "test_2025": {
-        "accuracy": 0.5762,
-        "f1_weighted": 0.5454
+        "accuracy": 0.5658,
+        "f1_weighted": 0.5396
       }
     },
     "XGBoost (n=300)": {
       "val_2024": {
-        "accuracy": 0.4489,
-        "f1_weighted": 0.4662
+        "accuracy": 0.428,
+        "f1_weighted": 0.4468
       },
       "test_2025": {
-        "accuracy": 0.5052,
-        "f1_weighted": 0.5118
+        "accuracy": 0.5094,
+        "f1_weighted": 0.515
       }
     },
     "GradBoost (n=200)": {
       "val_2024": {
-        "accuracy": 0.5616,
-        "f1_weighted": 0.5706
+        "accuracy": 0.4885,
+        "f1_weighted": 0.5039
       },
       "test_2025": {
-        "accuracy": 0.5303,
-        "f1_weighted": 0.5311
+        "accuracy": 0.5115,
+        "f1_weighted": 0.5177
       }
     }
   },
   "best_model_final": {
     "val_2024": {
       "baseline": {
-        "accuracy": 0.5616,
-        "f1_weighted": 0.5706,
-        "f1_macro": 0.5159
+        "accuracy": 0.5658,
+        "f1_weighted": 0.5517,
+        "f1_macro": 0.4656
       },
       "threshold": {
-        "accuracy": 0.5616,
-        "f1_weighted": 0.5706,
-        "f1_macro": 0.5159
+        "accuracy": 0.5658,
+        "f1_weighted": 0.5517,
+        "f1_macro": 0.4656
       }
     },
     "test_2025": {
       "baseline": {
-        "accuracy": 0.5303,
-        "f1_weighted": 0.5311,
-        "f1_macro": 0.498
+        "accuracy": 0.5658,
+        "f1_weighted": 0.5396,
+        "f1_macro": 0.4872
       },
       "threshold": {
-        "accuracy": 0.5303,
-        "f1_weighted": 0.5311,
-        "f1_macro": 0.498
+        "accuracy": 0.5658,
+        "f1_weighted": 0.5396,
+        "f1_macro": 0.4872
       }
     }
   }

requirements.txt

@@ -1,5 +1,5 @@
 pandas>=2.0.0
-numpy>=1.26.0
-scikit-learn>=1.4.0
+numpy==1.26.2
+scikit-learn==1.8.0
 openai>=1.30.0
 streamlit>=1.57.0