Create app.py

app.py

@@ -0,0 +1,34 @@
+import gradio as gr
+import torch
+
+# Load your trained model + tokenizer
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "your-username/your-trained-model"  # replace with your uploaded model repo
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def generate_text(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(
+        **inputs,
+        max_length=200,
+        temperature=0.7,
+        do_sample=True,
+        top_k=50
+    )
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## ✨ Custom Text Generator")
+    with gr.Row():
+        starter_prompt = gr.Textbox(label="Starter Prompt", placeholder="Type your prompt here...")
+    with gr.Row():
+        generate_btn = gr.Button("Generate Text")
+    with gr.Row():
+        output_box = gr.Textbox(label="Generated Result")
+
+    generate_btn.click(fn=generate_text, inputs=starter_prompt, outputs=output_box)
+
+demo.launch()