app.py

import torch
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# تحميل النموذج العربي
model_name = "CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

labels = ["negative", "neutral", "positive"]

# دالة تحليل المشاعر
def analyze_arabic_sentiment(text):
    if not text.strip():
        return "الرجاء إدخال نص"

    inputs = tokenizer(text, return_tensors="pt", truncation=True)
    outputs = model(**inputs)
    probs = torch.nn.functional.softmax(outputs.logits, dim=1)
    label_id = torch.argmax(probs).item()
    confidence = float(probs[0][label_id])

    return f"التصنيف: {labels[label_id]}\nدرجة الثقة: {round(confidence, 3)}"

# واجهة Gradio
with gr.Blocks() as demo:
    gr.Markdown("## 🔍 Arabic Sentiment Analyzer")
    gr.Markdown("اكتب نصًا عربيًا وسيتم تحديد نوع المشاعر.")

    text_input = gr.Textbox(label="النص")
    text_output = gr.Textbox(label="النتيجة")

    analyze_btn = gr.Button("تحليل")

    analyze_btn.click(fn=analyze_arabic_sentiment, inputs=text_input, outputs=text_output)

demo.launch()