Update app.py

app.py

@@ -1,48 +1,49 @@
-from fastapi import FastAPI, UploadFile, File
-import pandas as pd
-from sklearn.linear_model import LogisticRegression
-from sklearn.preprocessing import StandardScaler
-import io
-
-app = FastAPI(title="Employee of the Month API")
-
-# ------------------------
-# تدريب الموديل الأساسي
-# ------------------------
-df_train = pd.DataFrame({
-    'EmployeeID': [101, 102, 103, 104, 105, 106],
-    'PerformanceScore': [90, 85, 95, 80, 88, 92],
-    'ProjectsCompleted': [5, 6, 7, 4, 6, 5],
-    'Attendance': [98, 92, 95, 90, 97, 96],
-    'EmployeeOfTheMonth': [0, 0, 1, 0, 0, 0]
-})
-
-X_train = df_train[['PerformanceScore', 'ProjectsCompleted', 'Attendance']]
-y_train = df_train['EmployeeOfTheMonth']
-
-scaler = StandardScaler()
-X_train_scaled = scaler.fit_transform(X_train)
-
-model = LogisticRegression(class_weight='balanced')
-model.fit(X_train_scaled, y_train)
-
-# ------------------------
-# API Endpoint
-# ------------------------
-@app.post("/predict")
-async def predict_employee(file: UploadFile = File(...)):
-    # قراءة ملف Excel
-    contents = await file.read()
-    df_new = pd.read_excel(io.BytesIO(contents))
-
-    # تجهيز البيانات الجديدة
-    X_new_scaled = scaler.transform(df_new[['PerformanceScore', 'ProjectsCompleted', 'Attendance']])
-    probs = model.predict_proba(X_new_scaled)[:, 1]
-    df_new['ProbabilityOfBeingBest'] = probs
-
-    best_employee = df_new.loc[df_new['ProbabilityOfBeingBest'].idxmax()]
-    result = {
-        "BestEmployeeID": int(best_employee['EmployeeID']),
-        "Probability": float(best_employee['ProbabilityOfBeingBest']),
-    }
-    return result
+from fastapi import FastAPI, UploadFile, File
+import pandas as pd
+from sklearn.linear_model import LogisticRegression
+from sklearn.preprocessing import StandardScaler
+import io
+
+app = FastAPI(title="Employee of the Month API")
+
+# ------------------------
+# تدريب الموديل الأساسي
+# ------------------------
+df_train = pd.DataFrame({
+    'EmployeeID': [101, 102, 103, 104, 105, 106],
+    'PerformanceScore': [90, 85, 95, 80, 88, 92],
+    'ProjectsCompleted': [5, 6, 7, 4, 6, 5],
+    'Attendance': [98, 92, 95, 90, 97, 96],
+    'EmployeeOfTheMonth': [0, 0, 1, 0, 0, 0]
+})
+
+X_train = df_train[['PerformanceScore', 'ProjectsCompleted', 'Attendance']]
+y_train = df_train['EmployeeOfTheMonth']
+
+scaler = StandardScaler()
+X_train_scaled = scaler.fit_transform(X_train)
+
+model = LogisticRegression(class_weight='balanced')
+model.fit(X_train_scaled, y_train)
+
+# ------------------------
+# API Endpoint
+# ------------------------
+@app.post("/predict")
+async def predict_employee(file: UploadFile = File(...)):
+    # قراءة ملف Excel
+    contents = await file.read()
+    df_new = pd.read_excel(io.BytesIO(contents))
+
+    # تجهيز البيانات الجديدة
+    X_new_scaled = scaler.transform(df_new[['PerformanceScore', 'ProjectsCompleted', 'Attendance']])
+    probs = model.predict_proba(X_new_scaled)[:, 1]
+    df_new['ProbabilityOfBeingBest'] = probs
+
+    best_employee = df_new.loc[df_new['ProbabilityOfBeingBest'].idxmax()]
+    result = {
+        "BestEmployeeID": int(best_employee['EmployeeID']),
+        "Probability": float(best_employee['ProbabilityOfBeingBest']),
+    }
+    return result
+    iface.launch(server_name="0.0.0.0", server_port=7860)