Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import os
+import spaces
+import torch
+
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+print(f'[INFO] Using device: {device}')
+
+# token
+token = os.environ['TOKEN']
+
+# Load the pretrained model and tokenizer
+MODEL_NAME = "atlasia/Al-Atlas-LLM"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, token=token)
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, token=token).to(device)
+
+# Predefined examples
+examples = [
+    ["الذكاء الاصطناعي هو فرع من علوم الكمبيوتر اللي كيركز"
+     , 256, 0.7, 0.9, 150, 8, 1.5],
+    ["المستقبل ديال الذكاء الصناعي فالمغرب"
+     , 256, 0.7, 0.9, 150, 8, 1.5],
+    [" المطبخ المغربي"
+     , 256, 0.7, 0.9, 150, 8, 1.5],
+    ["الماكلة المغربية كتعتبر من أحسن الماكلات فالعالم"
+     , 256, 0.7, 0.9, 150, 8, 1.5],
+]
+
+@spaces.GPU
+def generate_text(prompt, max_length=256, temperature=0.7, top_p=0.9, top_k=150, num_beams=8, repetition_penalty=1.5):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    output = model.generate(
+        **inputs, 
+        max_length=max_length, 
+        temperature=temperature, 
+        top_p=top_p, 
+        do_sample=True,
+        repetition_penalty=repetition_penalty,
+        num_beams=num_beams,
+        top_k= top_k,
+        early_stopping = True,
+    )
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+if __name__ == "__main__":
+    # Create the Gradio interface
+    with gr.Blocks() as app:
+        gr.Interface(
+            fn=generate_text,
+            inputs=[
+                gr.Textbox(label="Prompt: دخل النص بالدارجة"),
+                gr.Slider(50, 4096, value=256, label="Max Length"),
+                gr.Slider(0.1, 1.5, value=0.7, label="Temperature"),
+                gr.Slider(0.1, 1.0, value=0.9, label="Top-p"),
+                gr.Slider(1, 10000, value=150, label="Top-k"),
+                gr.Slider(1, 20, value=8, label="Number of Beams"),
+                gr.Slider(0.0, 100.0, value=1.5, label="Repetition Penalty"),
+            ],
+            outputs=gr.Textbox(label="Generated Text in Moroccan Darija"),
+            title="Moroccan Darija LLM",
+            description="Enter a prompt and get AI-generated text using our pretrained LLM on Moroccan Darija.",
+            examples=examples,
+        )
+    
+    app.launch()