Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+
+# Load the pre-trained GPT-2 model and tokenizer
+model_name = "gpt2"  # You can change this to another model if needed
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+model = GPT2LMHeadModel.from_pretrained(model_name)
+
+def generate_text(prompt):
+    # Encode the input text and generate a continuation
+    input_ids = tokenizer.encode(prompt, return_tensors="pt")
+    output = model.generate(input_ids, max_length=100, num_return_sequences=1, pad_token_id=50256)
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    
+    # Save the generated text to a text file
+    with open("generated_text.txt", "w") as file:
+        file.write(generated_text)
+    
+    return generated_text
+
+iface = gr.Interface(fn=generate_text, inputs="text", outputs="text")
+iface.launch()