Created app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+tokenizer = AutoTokenizer.from_pretrained("fubuki119/JokesGPT")
+model = AutoModelForCausalLM.from_pretrained("fubuki119/JokesGPT")
+
+
+def generate(max_length):
+    starting_text = "JOKE:"
+    end_token = "<|endoftext|>"
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model.to(device)
+    model.eval()
+    with torch.no_grad():
+        cur_ids = torch.tensor(tokenizer.encode("JOKE:")
+                               ).unsqueeze(0).to(device)
+        for i in range(max_length):
+            outputs = model(cur_ids)
+            logits, _ = outputs[:]
+            softmax_logits = torch.softmax(logits[0, -1], dim=0)
+
+            next_token_id = torch.multinomial(softmax_logits, 1).item()
+            cur_ids = torch.cat([cur_ids, torch.ones(
+                (1, 1)).long().to(device) * next_token_id], dim=1)
+            if next_token_id == tokenizer.encode(end_token)[0]:
+                joke = cur_ids.detach().cpu().tolist()
+                joke = joke[0]
+                return tokenizer.decode(joke[3:-1])
+        joke = cur_ids.detach().cpu().tolist()
+        joke = joke[0]
+        return tokenizer.decode(joke[3:])
+
+
+def fn(n):
+    print(f"i got {n}")
+    return "thanks"
+
+
+iface = gr.Interface(
+    fn=generate,
+    inputs=gr.Number(value=200, label="Maxlen"),
+    outputs="text",
+)
+iface.launch(share=True, debug=True)