update

app.py

@@ -1,14 +1,28 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load the translation pipeline
-model_name = "AbdulmohsenA/Faseeh"  # Replace with your model name
-translator = pipeline("translation_en_to_ar", model=model_name)
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+import warnings
 
-# Define translation function
-def translate_text(text):
-    result = translator(text)
-    return result[0]['translation_text']
+warnings.filterwarnings("ignore")
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("Abdulmohsena/Faseeh")
+model = AutoModelForSeq2SeqLM.from_pretrained("Abdulmohsena/Faseeh").to(device)
+
+def translate(text, temperature=0.1, tries=1):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=256).to(device)
+    outputs = model.generate(
+        **inputs,
+        do_sample=True,
+        temperature=temperature,
+        num_return_sequences=tries,
+    )
+    translation = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+    return translation
 
 # Gradio interface
 with gr.Blocks() as demo:
@@ -24,7 +38,7 @@ with gr.Blocks() as demo:
     translate_btn = gr.Button("Translate")
 
     # Button action
-    translate_btn.click(translate_text, inputs=input_text, outputs=output_text)
+    translate_btn.click(translate, inputs=input_text, outputs=output_text)
 
 # Launch the Gradio app
 if __name__ == "__main__":