Update app.py

app.py

@@ -1,31 +1,31 @@
-from fastapi import FastAPI
-import joblib
-import uvicorn
-
-app = FastAPI()
-
-model = joblib.load('ridge_model.pkl')
-poly = joblib.load('polynomial_transformer.pkl')
-
-def predict_corrected_rank(percentile: float, total_candidates: int) -> float:
-    # Calculate initial predicted rank using the formula
-    predicted_rank = ((100 - percentile) * total_candidates) / 100
-    # Predict correction factor using the polynomial regression model
-    percentile_poly = poly.transform([[percentile]])
-    predicted_correction = model.predict(percentile_poly)[0][0]
-    # Adjust the predicted rank with the correction factor
-    corrected_rank = predicted_rank + predicted_correction
-    # Ensure the rank does not exceed the total number of candidates or become negative
-    corrected_rank = max(1, min(corrected_rank, total_candidates))
-    return corrected_rank
-
-
-@app.get("/predict")
-def get_corrected_rank(percentile: float, total_candidates: int):
-    corrected_rank = predict_corrected_rank(percentile, total_candidates)
-    return {"percentile": percentile, "total_candidates": total_candidates, "corrected_rank": corrected_rank}
-
-
-if __name__ == "__main__":
-    # logger.info("Starting PreCollege Data Scraper Server...")
+from fastapi import FastAPI
+import joblib
+import uvicorn
+
+app = FastAPI()
+
+model = joblib.load('ridge_model.pkl')
+poly = joblib.load('polynomial_transformer.pkl')
+
+def predict_corrected_rank(percentile: float, total_candidates: int) -> float:
+    # Calculate initial predicted rank using the formula
+    predicted_rank = ((100 - percentile) * total_candidates) / 100
+    # Predict correction factor using the polynomial regression model
+    percentile_poly = poly.transform([[percentile]])
+    predicted_correction = model.predict(percentile_poly)[0][0]
+    # Adjust the predicted rank with the correction factor
+    corrected_rank = predicted_rank + predicted_correction
+    # Ensure the rank does not exceed the total number of candidates or become negative
+    corrected_rank = max(1, min(corrected_rank, total_candidates))
+    return round(corrected_rank)
+
+
+@app.get("/predict")
+def get_corrected_rank(percentile: float, total_candidates: int):
+    corrected_rank = predict_corrected_rank(percentile, total_candidates)
+    return {"percentile": percentile, "total_candidates": total_candidates, "corrected_rank": corrected_rank}
+
+
+if __name__ == "__main__":
+    # logger.info("Starting PreCollege Data Scraper Server...")
     uvicorn.run(app, host="0.0.0.0", port=7860)