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app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load pre-trained model and tokenizer
+model_name = "gpt2"  # You can also try "distilgpt2" for a smaller model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Function to generate text
+def generate_text(prompt, max_length=100, num_return_sequences=1):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(
+        inputs["input_ids"],
+        max_length=max_length,
+        num_return_sequences=num_return_sequences,
+        no_repeat_ngram_size=2,
+        top_p=0.95,
+        top_k=60,
+        temperature=0.7,
+    )
+    return [tokenizer.decode(output, skip_special_tokens=True) for output in outputs]
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.Textbox(label="Enter your prompt", placeholder="Type here..."),
+        gr.Slider(minimum=50, maximum=200, value=100, label="Max Length"),
+        gr.Slider(minimum=1, maximum=5, value=1, label="Number of Outputs")
+    ],
+    outputs=gr.Textbox(label="Generated Text"),
+    title="GPT-2 Text Generator",
+    description="Generate human-like text based on your prompt using GPT-2.",
+    theme="compact",
+    examples=[["Once upon a time in a land far, far away..."]]
+)
+
+iface.launch()

requirements.txt

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+gradio
+transformers