| import gradio as gr | |
| from transformers import pipeline | |
| # Load the llama2 LLM model | |
| model = pipeline("text-generation", model="llamalanguage/llama2", tokenizer="llamalanguage/llama2") | |
| # Define the chat function that uses the LLM model | |
| def chat_interface(input_text): | |
| response = model(input_text, max_length=100, return_full_text=True)[0]["generated_text"] | |
| response_words = response.split() | |
| return response_words | |
| # Create the Gradio interface | |
| iface = gr.Interface( | |
| fn=chat_interface, | |
| inputs=gr.inputs.Textbox(lines=2, label="Input Text"), | |
| outputs=gr.outputs.Textbox(label="Output Text"), | |
| title="Chat Interface", | |
| description="Enter text and get a response using the LLM model", | |
| live=True # Enable live updates | |
| ) | |
| # Launch the interface using Hugging Face Spaces | |
| iface.launch(share=True) |