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import gradio as gr
from transformers import GPT2LMHeadModel, GPT2Tokenizer
# Load model and tokenizer
model_name = "gpt2"
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
# Function to filter explicit content
def filter_explicit(content, filter_on):
explicit_keywords = ["badword1", "badword2"] # Add explicit words to filter
if filter_on:
for word in explicit_keywords:
content = content.replace(word, "[CENSORED]")
return content
def generate_response(prompt, explicit_filter):
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(inputs, max_length=100, num_return_sequences=1)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
filtered_response = filter_explicit(response, explicit_filter)
return filtered_response
# Define Gradio interface
iface = gr.Interface(
fn=generate_response,
inputs=[gr.inputs.Textbox(lines=2, placeholder="Type your message here..."), gr.inputs.Checkbox(label="Enable Explicit Content Filter")],
outputs="text",
title="Chatbot with Explicit Content Filter"
)
if __name__ == "__main__":
iface.launch()
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