Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+# Load the model and tokenizer
+model = GPT2LMHeadModel.from_pretrained("samwell/SamGPT")
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")  # Assuming you used the GPT-2 tokenizer
+
+def generate_text(prompt, max_length):
+    input_ids = tokenizer.encode(prompt, return_tensors="pt")
+    output = model.generate(input_ids, max_length=max_length, num_return_sequences=1, no_repeat_ngram_size=2)
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    return generated_text
+
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.Textbox(label="Prompt"),
+        gr.Slider(minimum=10, maximum=200, value=50, step=1, label="Max Length")
+    ],
+    outputs=gr.Textbox(label="Generated Text"),
+    title="SamGPT Text Generation",
+    description="Enter a prompt to generate text with SamGPT."
+)
+
+iface.launch()