Create app.py

app.py

@@ -0,0 +1,41 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import gradio as gr
+
+# 1) Load the Central Kurdish (Arabic) Goldfish model
+MODEL_ID = "goldfish-models/ckb_arab_full"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(MODEL_ID)
+model.eval()
+
+# 2) Chat function: maintains history and generates replies
+def chat_fn(user_message, history):
+    # Prepend [CLS] token and append [SEP]
+    prompt = tokenizer.cls_token + user_message + tokenizer.sep_token
+    inputs = tokenizer(prompt, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=128,
+            pad_token_id=tokenizer.eos_token_id,
+            do_sample=True,
+            top_p=0.9,
+            temperature=0.8
+        )
+    # Decode only the newly generated tokens
+    reply = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
+    history = history + [(user_message, reply)]
+    return history, history
+
+# 3) Build Gradio Chat UI
+with gr.Blocks() as demo:
+    gr.Markdown("## Chat with Goldfish’s Central Kurdish (Arabic) Model")
+    chatbot = gr.Chatbot()
+    txt = gr.Textbox(placeholder="Type your message here...")
+    clear = gr.Button("Clear")
+    txt.submit(chat_fn, [txt, chatbot], [chatbot, chatbot])
+    clear.click(lambda: None, None, chatbot)
+    
+# 4) Launch the app
+if __name__ == "__main__":
+    demo.launch()