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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+example2.png filter=lfs diff=lfs merge=lfs -text

main.py

@@ -0,0 +1,71 @@
+"""
+OCR & Translate — slicker UI with big title
+> pip install easyocr pillow sacremoses torch torchvision torchaudio gradio
+"""
+
+import gradio as gr
+import numpy as np
+import easyocr
+from PIL import Image
+from transformers import MarianMTModel, MarianTokenizer
+import torch
+
+# ─────── Models ───────
+LOCAL_MODEL_PATH = r"C:\Users\96658\Desktop\NLP Project\NLP Project\Models\en-ar-transformer_model"
+device     = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+reader     = easyocr.Reader(['en'], gpu=torch.cuda.is_available())
+tokenizer  = MarianTokenizer.from_pretrained(LOCAL_MODEL_PATH)
+translator = MarianMTModel.from_pretrained(LOCAL_MODEL_PATH).to(device)
+
+def ocr_and_translate(img: Image.Image):
+    lines = reader.readtext(np.array(img), detail=0, paragraph=True)
+    text  = "\n".join(lines).strip()
+    if not text:
+        return "", "No text detected."
+    toks = tokenizer([text], return_tensors="pt",
+                     padding=True, truncation=True).to(device)
+    with torch.no_grad():
+        out = translator.generate(**toks, max_length=512)
+    arabic = tokenizer.batch_decode(out, skip_special_tokens=True)[0]
+    return text, arabic
+
+# ─────── Theme & CSS ───────
+nice_theme = gr.themes.Soft(primary_hue="cyan", neutral_hue="stone")
+
+custom_css = """
+#container {max-width: 1000px; margin: auto;}
+#main_title {
+    font-size: 120px;          
+    font-weight: 1000;
+    text-align: center;
+    margin: 1rem 0;
+}
+footer {visibility: hidden;}  /* hide “Powered by Gradio” */
+.gr-box, .gr-button, .gr-image {border-radius: 6px;}
+"""
+
+# ─────── UI ───────
+with gr.Blocks(theme=nice_theme, css=custom_css) as demo:
+    with gr.Column(elem_id="container"):
+        gr.Markdown("# 📝 ترجمان", elem_id="main_title")     # BIG title
+        with gr.Row():
+            img_in = gr.Image(label="Drop or click to upload",
+                              type="pil",
+                              height=350)
+            with gr.Column():
+                txt_out   = gr.Textbox(label="Extracted Text (EN)", lines=8)
+                trans_out = gr.Textbox(label="Translation (AR)",   lines=8)
+        btn = gr.Button("🔍 Extract & Translate", variant="primary")
+        btn.click(ocr_and_translate, img_in, [txt_out, trans_out])
+
+    gr.Examples(
+        examples=[
+            "example1.png",
+            "example2.png"
+        ],
+        inputs=img_in,
+        label="Try an example"
+    )
+
+if __name__ == "__main__":
+    demo.launch(debug=True)