Create main.py

main.py

@@ -0,0 +1,70 @@
+import os
+import pickle
+import pandas as pd
+from fastapi import FastAPI, Request, Form
+from fastapi.responses import HTMLResponse
+from fastapi.templating import Jinja2Templates
+
+app = FastAPI()
+# Templates folder
+templates = Jinja2Templates(directory="templates")
+
+# Load trained model (ensure student_performance_model.pkl is in /app)
+MODEL_PATH = os.getenv('MODEL_PATH', 'student_performance_model.pkl')
+with open(MODEL_PATH, 'rb') as f:
+    model = pickle.load(f)
+
+@app.get('/', response_class=HTMLResponse)
+async def read_form(request: Request):
+    return templates.TemplateResponse('index.html', {
+        'request': request,
+        'predicted': None
+    })
+
+@app.post('/predict', response_class=HTMLResponse)
+async def predict(request: Request,
+                  Age: int = Form(...),
+                  Gender: str = Form(...),
+                  HoursOfStudyPerDay: float = Form(...),
+                  SchoolAttendanceRate: float = Form(...),
+                  TuitionAccess: str = Form(...),
+                  AveragePreviousScores: float = Form(...),
+                  HoursOfSleep: float = Form(...),
+                  BreakfastDaily: str = Form(...),
+                  ScreenTimeHours: float = Form(...),
+                  PhysicalActivityHours: float = Form(...),
+                  PlaysSport: str = Form(...),
+                  MentalHealthScore: float = Form(...),
+                  ParentalEducationLevel: str = Form(...),
+                  HouseholdIncomeLevel: str = Form(...),
+                  StudyEnvironmentRating: float = Form(...),
+                  FriendSupportScore: float = Form(...),
+                  ParticipatesInClubs: str = Form(...),
+                  PartTimeWork: str = Form(...)):
+    # Organize input into DataFrame
+    data = pd.DataFrame([{
+        'Age': Age,
+        'Gender': Gender,
+        'HoursOfStudyPerDay': HoursOfStudyPerDay,
+        'SchoolAttendanceRate': SchoolAttendanceRate,
+        'TuitionAccess': TuitionAccess,
+        'AveragePreviousScores': AveragePreviousScores,
+        'HoursOfSleep': HoursOfSleep,
+        'BreakfastDaily': BreakfastDaily,
+        'ScreenTimeHours': ScreenTimeHours,
+        'PhysicalActivityHours': PhysicalActivityHours,
+        'PlaysSport': PlaysSport,
+        'MentalHealthScore': MentalHealthScore,
+        'ParentalEducationLevel': ParentalEducationLevel,
+        'HouseholdIncomeLevel': HouseholdIncomeLevel,
+        'StudyEnvironmentRating': StudyEnvironmentRating,
+        'FriendSupportScore': FriendSupportScore,
+        'ParticipatesInClubs': ParticipatesInClubs,
+        'PartTimeWork': PartTimeWork
+    }])
+    # Predict
+    pred = model.predict(data)[0]
+    return templates.TemplateResponse('index.html', {
+        'request': request,
+        'predicted': round(pred, 2)
+    })