Update app.py

app.py

@@ -1,11 +1,9 @@
-from fastapi import FastAPI, UploadFile, File
+import gradio as gr
 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
 # ------------------------
@@ -27,22 +25,31 @@ model = LogisticRegression(class_weight='balanced')
 model.fit(X_train_scaled, y_train)
 
 # ------------------------
-# API Endpoint
+# دالة التنبؤ
 # ------------------------
-@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))
+def predict_employee(file):
+    df_new = pd.read_excel(file)
 
-    # 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 {
+        "Best Employee ID": int(best_employee['EmployeeID']),
+        "Probability": float(best_employee['ProbabilityOfBeingBest'])
     }
-    return result
+
+# ------------------------
+# واجهة Gradio
+# ------------------------
+iface = gr.Interface(
+    fn=predict_employee,
+    inputs=gr.File(label="Upload Excel File (.xlsx)"),
+    outputs="json",
+    title="Employee of the Month Predictor",
+    description="Upload an Excel file containing 'EmployeeID', 'PerformanceScore', 'ProjectsCompleted', and 'Attendance' columns to find the best employee."
+)
+
+iface.launch()