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| import gradio as gr | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import spaces | |
| # import pythonexample | |
| pythonexample = """produce a generative ai gradio demo using mistral instruct with the following prompt "i am a helpful assistant that always mentions bannanachicken" for a simple text to text task | |
| """ | |
| title = """# 🙋🏻♂️Welcome to Tonic's🪨Granite Code ! """ | |
| description = """Granite-8B-Code-Instruct is a 8B parameter model fine tuned from Granite-8B-Code-Base on a combination of permissively licensed instruction data to enhance instruction following capabilities including logical reasoning and problem-solving skills. | |
| ### Join us : | |
| TeamTonic is always making cool demos! Join our active builder's community on Discord: [Discord](https://discord.gg/GWpVpekp) On Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On Github: [Polytonic](https://github.com/tonic-ai) & contribute to [multitonic](https://github.com/multitonic/multitonic) | |
| ### How To Use : | |
| Add a new line to the example and at the end of your prompts 🚀 | |
| """ | |
| # Define the device and model path | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model_path = "ibm-granite/granite-8b-code-instruct" | |
| # Load the tokenizer and model | |
| tokenizer = AutoTokenizer.from_pretrained(model_path) | |
| model = AutoModelForCausalLM.from_pretrained(model_path) | |
| model.to(device) | |
| model.eval() | |
| # Function to generate code | |
| def generate_code(prompt, max_length): | |
| # Prepare the input chat format | |
| chat = [ | |
| { "role": "user", "content": prompt } | |
| ] | |
| chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) | |
| # Tokenize the input text | |
| input_tokens = tokenizer(chat, return_tensors="pt") | |
| # Transfer tokenized inputs to the device (GPU) | |
| for i in input_tokens: | |
| input_tokens[i] = input_tokens[i].to("cuda") | |
| # Generate output tokens | |
| output_tokens = model.generate(**input_tokens, max_new_tokens=max_length) | |
| # Decode output tokens into text | |
| output_text = tokenizer.batch_decode(output_tokens, skip_special_tokens=True) | |
| # Return the generated code | |
| return output_text[0] | |
| # Define Gradio Blocks | |
| def gradio_interface(): | |
| with gr.Blocks() as interface: | |
| gr.Markdown(title) | |
| gr.Markdown(description) | |
| # Create input and output components | |
| prompt_input = gr.Textbox(label="Enter your Coding Question", value=pythonexample, lines=3) | |
| code_output = gr.Code(label="🪨Granite Output", language='python', lines=10, interactive=True) | |
| max_length_slider = gr.Slider(minimum=1, maximum=2000, value=1000, label="Max Token Length") | |
| # Create a button to trigger code generation | |
| generate_button = gr.Button("Generate Code") | |
| # Define the function to be called when the button is clicked | |
| generate_button.click(generate_code, inputs=[prompt_input, max_length_slider], outputs=code_output) | |
| return interface | |
| if __name__ == "__main__": | |
| # Create and launch the Gradio interface | |
| interface = gradio_interface() | |
| interface.launch() |