Create app.py

app.py

@@ -0,0 +1,40 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("jsbeaudry/makandal-pre-trained")
+model = AutoModelForCausalLM.from_pretrained("jsbeaudry/makandal-pre-trained")
+
+# Set device
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+# Generation function
+def generate_text(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt", padding=True).to(device)
+    output = model.generate(
+        **inputs,
+        max_new_tokens=100,
+        do_sample=True,
+        repetition_penalty=1.2,
+        no_repeat_ngram_size=3,
+        temperature=0.9,
+        top_k=40,
+        top_p=0.85,
+        pad_token_id=tokenizer.pad_token_id,
+        eos_token_id=tokenizer.eos_token_id
+    )
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+# Gradio interface
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=gr.Textbox(lines=2, placeholder="Ekri yon sijè oswa yon fraz..."),
+    outputs="text",
+    title="Makandal Text Generator",
+    description="Ekri yon fraz oswa mo kle pou jenere tèks ak modèl Makandal la. Modèl sa fèt espesyalman pou kontèks Ayiti."
+)
+
+if __name__ == "__main__":
+    iface.launch()