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Gptt
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import gradio as gr
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Load the uncensoredgpt model and tokenizer
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model_name = "gpt2" # Replace with the actual uncensoredgpt model name if available
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model = AutoModelForCausalLM.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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def generate_text(prompt, max_length=100):
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inputs = tokenizer.encode(prompt, return_tensors="pt")
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outputs = model.generate(inputs, max_length=max_length, num_return_sequences=1)
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generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return generated_text
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# Create the Gradio interface
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iface = gr.Interface(
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fn=generate_text,
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inputs=gr.inputs.Textbox(lines=2, placeholder="Enter your prompt here..."),
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outputs="text",
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title="UncensoredGPT",
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description="A simple interface for the uncensoredgpt model."
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)
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# Launch the interface
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iface.launch()
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