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README.md

@@ -1,13 +1,24 @@
----
-title: MSA
-emoji: 🏃
-colorFrom: gray
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.44.1
-app_file: app.py
-pinned: false
-short_description: Translator
----
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# MSA Translator
+
+**MSA Translator** is a Streamlit web application that translates Egyptian Arabic dialect into Modern Standard Arabic (MSA) using a fine-tuned transformer model.
+
+## ✨ Features
+
+- Real-time translation of dialectal Arabic to MSA.
+- Powered by the `PRAli22/arat5-arabic-dialects-translation` model.
+- Simple and user-friendly web interface.
+- Built with Streamlit and Hugging Face Transformers.
+
+## 🚀 How to Run
+
+```bash
+pip install -r requirements.txt
+streamlit run msa_translator_app.py
+```
+
+## 🌐 Demo
+
+You can deploy this app easily on [Hugging Face Spaces](https://huggingface.co/spaces) with Streamlit UI.
+
+---

msa_translator_app.py

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+
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+
+# تحميل النموذج والـ tokenizer
+@st.cache_resource
+def load_model():
+    model_name = "PRAli22/arat5-arabic-dialects-translation"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+# تصميم الواجهة
+st.set_page_config(page_title="MSA Translator", layout="centered")
+st.title("🔁 MSA Translator")
+st.markdown("ترجم من اللهجة المصرية إلى العربية الفصحى باستخدام الذكاء الاصطناعي.")
+
+user_input = st.text_area("🗣️ اكتب جملة باللهجة المصرية", height=100)
+
+if st.button("ترجم"):
+    if user_input.strip() == "":
+        st.warning("من فضلك اكتب جملة باللهجة.")
+    else:
+        inputs = tokenizer(user_input, return_tensors="pt", padding=True)
+        with torch.no_grad():
+            outputs = model.generate(**inputs, max_length=50, num_beams=5, early_stopping=True)
+        translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        st.success("✅ الترجمة الفصحى:")
+        st.write(f"📜 {translated_text}")

requirements.txt

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+
+streamlit
+transformers
+torch