Update app.py

app.py

@@ -1,50 +1,59 @@
 import gradio as gr
 from transformers import pipeline
-import requests
 import json
+import os
 from fastapi import FastAPI
-from gradio.routes import App as GradioApp
 
-# Model
-model_name = "Woolv7007/egyptian-text-classification"
-pipe = pipeline("text-classification", model=model_name)
-
-# Load labels
-labels_url = f"https://huggingface.co/{model_name}/resolve/main/labels.json"
+# تحميل التصنيفات من ملف محلي
+labels_path = os.path.join(os.path.dirname(__file__), "labels.json")
 try:
-    response = requests.get(labels_url)
-    response.raise_for_status()
-    labels = response.json()
+    with open(labels_path, "r", encoding="utf-8") as f:
+        labels = json.load(f)
     if isinstance(labels, dict):
         labels = list(labels.values())
-except:
+    print("Labels loaded:", labels)
+except Exception as e:
+    print("Failed to load local labels.json:", e)
     labels = None
 
+# تحميل النموذج
+model_name = "Woolv7007/egyptian-text-classification"
+pipe = pipeline("text-classification", model=model_name)
+print("Model loaded.")
+
+# دالة التنبؤ
 def predict(text):
-    result = pipe(text)[0]
-    label_id = int(result['label'].replace("LABEL_", ""))
-    label_text = labels[label_id] if labels and label_id < len(labels) else result['label']
-    prediction_bool = label_text.lower() in ["ads", "neutral"]
-    confidence = round(result['score'], 3)
-
-    return str(prediction_bool), json.dumps({
-        "prediction": prediction_bool,
-        "original_label": label_text,
-        "confidence": confidence
-    }, indent=4, ensure_ascii=False)
-
-# Gradio Interface
+    try:
+        result = pipe(text)[0]
+        label_id = int(result['label'].replace("LABEL_", ""))
+        label_text = labels[label_id] if labels and label_id < len(labels) else result['label']
+        prediction_bool = label_text.lower() in ["ads", "neutral"]
+        confidence = round(result['score'], 3)
+
+        json_output = {
+            "prediction": prediction_bool,
+            "original_label": label_text,
+            "confidence": confidence
+        }
+
+        return str(prediction_bool), json.dumps(json_output, indent=4, ensure_ascii=False)
+
+    except Exception as e:
+        error_msg = str(e)
+        return "Error", json.dumps({"error": error_msg}, indent=4, ensure_ascii=False)
+
+# واجهة Gradio
 demo = gr.Interface(
     fn=predict,
-    inputs=gr.Textbox(lines=3, placeholder="Enter Egyptian Arabic text..."),
+    inputs=gr.Textbox(lines=3, placeholder="أدخل نصًا باللهجة المصرية..."),
     outputs=[
         gr.Textbox(label="Prediction (True/False)"),
         gr.Textbox(label="Full JSON Output")
     ],
     title="Egyptian Text Classification",
-    description="Only 'ads' and 'neutral' are considered True."
+    description="هذا النموذج يصنف النصوص المكتوبة باللهجة المصرية. فقط التصنيفات 'ads' و 'neutral' تعتبر True."
 )
 
-# FastAPI + Gradio معًا
+# إنشاء تطبيق FastAPI وتوصيله بـ Gradio
 app = FastAPI()
-app = gr.mount_gradio_app(app, demo, path="/")  # UI on "/"
+app = gr.mount_gradio_app(app, demo, path="/")