main Update main.py

main.py

@@ -1,14 +1,60 @@
-from typing import Optional
-
-from fastapi import FastAPI
+# Save this as main.py
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import joblib
+import pandas as pd
 
 app = FastAPI()
 
+class StudentData(BaseModel):
+    code_module: str
+    code_presentation: str
+    gender: str
+    region: str
+    highest_education: str
+    age_band: str
+    num_of_prev_attempts: int
+    Student_sum_click: int
+    disability: str
+
+# Load the model
+try:
+    model = joblib.load('student_performance_model.joblib')
+except FileNotFoundError:
+    raise HTTPException(status_code=500, detail="Model file not found. Please ensure the model is trained and saved correctly.")
+
+@app.post("/predict")
+def predict(data: StudentData):
+    # Convert input data to DataFrame
+    input_df = pd.DataFrame([data.dict()])
+
+    # Make prediction
+    try:
+        prediction = model.predict(input_df)
+        return {"predicted_performance": prediction[0]}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}")
+
+@app.get("/model_info")
+def model_info():
+    return {
+        "model_type": "Random Forest Classifier",
+        "best_parameters": {
+            "max_depth": 10,
+            "min_samples_leaf": 4,
+            "min_samples_split": 2,
+            "n_estimators": 300
+        },
+        "accuracy": 0.4333,
+        "supported_classes": ["Distinction", "Fail", "Pass", "Withdrawn"]
+    }
 
 @app.get("/")
-async def root():
-    return {"message": "Hello World"}
+def read_root():
+    return {"status": "ok", "message": "Student Performance Prediction API is running"}
+
+
 
-@app.get("/items/{item_id}")
-def read_item(item_id: int, q: Optional[str] = None):
-    return {"item_id": item_id, "q": q}
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8080)