Update app.py
Browse files
app.py
CHANGED
@@ -8,25 +8,38 @@ tokenizer = GPT2Tokenizer.from_pretrained(model_name)
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# Function to filter explicit content
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def filter_explicit(content, filter_on):
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explicit_keywords = ["badword1", "badword2"] # Add explicit words to filter
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if filter_on:
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for word in explicit_keywords:
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content = content.replace(word, "[CENSORED]")
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return content
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def generate_response(prompt, explicit_filter):
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# Define Gradio interface
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iface = gr.Interface(
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fn=generate_response,
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inputs=[gr.Textbox(lines=2, placeholder="Type your message here..."), gr.Checkbox(label="Enable Explicit Content Filter")],
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outputs="
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title="Chatbot with Explicit Content Filter"
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)
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if __name__ == "__main__":
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# Function to filter explicit content
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def filter_explicit(content, filter_on):
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explicit_keywords = ["badword1", "badword2", "badword3"] # Add more explicit words to filter
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if filter_on:
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for word in explicit_keywords:
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content = content.replace(word, "[CENSORED]")
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return content
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def generate_response(prompt, explicit_filter):
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try:
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inputs = tokenizer.encode(prompt, return_tensors="pt")
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outputs = model.generate(
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inputs,
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max_length=100,
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num_return_sequences=1,
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temperature=0.7, # Control the creativity of the response
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top_k=50, # Limits the sampling pool to top 50 tokens
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top_p=0.9 # Nucleus sampling to avoid repetitive phrases
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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filtered_response = filter_explicit(response, explicit_filter)
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return filtered_response
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except Exception as e:
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return f"Error: {str(e)}"
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# Define Gradio interface
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iface = gr.Interface(
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fn=generate_response,
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inputs=[gr.Textbox(lines=2, placeholder="Type your message here...", label="Input"), gr.Checkbox(label="Enable Explicit Content Filter")],
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outputs=gr.Textbox(label="Response"),
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title="Chatbot with Explicit Content Filter",
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description="A simple chatbot that allows you to enable or disable explicit content filtering.",
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theme="compact",
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layout="vertical"
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)
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if __name__ == "__main__":
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