Upload app.py

app.py

@@ -0,0 +1,49 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "EleutherAI/gpt-neo-125M"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+def generate_text(prompt, max_new_tokens=80):
+    inputs = tokenizer(prompt, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            do_sample=True,
+            temperature=0.9,
+            top_p=0.95,
+            repetition_penalty=1.1,
+            pad_token_id=tokenizer.eos_token_id
+        )
+
+    full_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    generated = full_text[len(prompt):]
+
+    return f"<span style='color:#00ff88'>{generated}</span>"
+
+with gr.Blocks(css="textarea {font-family: monospace;}") as demo:
+    gr.Markdown("# 🧠 TextPlayground")
+
+    with gr.Row():
+        prompt = gr.Textbox(
+            label="Input",
+            placeholder="Type something like: Once upon a time...",
+            lines=5
+        )
+
+    output = gr.HTML(label="Completion")
+
+    generate_btn = gr.Button("Generate ⚡")
+
+    generate_btn.click(
+        fn=generate_text,
+        inputs=[prompt],
+        outputs=[output]
+    )
+
+demo.launch()