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| import gradio as gr | |
| from transformers import pipeline | |
| # Initialize the text generation pipeline with a suitable model | |
| model_name = "meta-llama/Llama-3.2-1B" # You can replace this with any legal-specific model if needed | |
| pipe = pipeline("text-generation", model=model_name) | |
| # Function to generate legal responses | |
| def legal_assistant(query): | |
| # Format the input with context for legal advice | |
| input_text = f"Legal Assistant: {query}\nProvide me with a legal perspective or explanation." | |
| result = pipe(input_text, max_length=200, num_return_sequences=1) | |
| return result[0]["generated_text"] | |
| # Create the Gradio interface | |
| iface = gr.Interface( | |
| fn=legal_assistant, | |
| inputs=gr.Textbox(label="Ask the AI Advocate a Legal Question"), | |
| outputs=gr.Textbox(label="Legal Response"), | |
| title="Code Legalist - Your AI Legal Advocate", | |
| description="Ask any legal-related questions, and get answers from the AI-powered advocate assistant.", | |
| ) | |
| # Launch the interface | |
| iface.launch(share=True) | |