| import os | |
| import gradio as gr | |
| from huggingface_hub import login | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| # Fetch API token from environment variable | |
| api_token = os.getenv("Llama_Token") | |
| # Authenticate with Hugging Face | |
| login(api_token) | |
| # Load LLaMA 3.2 model and tokenizer with the API token | |
| model_name = "meta-llama/Llama-3.2-1B" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name, token=api_token) | |
| model = AutoModelForCausalLM.from_pretrained(model_name, token=api_token) | |
| pipe = pipeline("text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto") | |
| pipe("How are you doing?") | |
| # # Define the inference function | |
| # def generate_text(prompt, max_length, temperature): | |
| # inputs = tokenizer(prompt, return_tensors="pt") | |
| # output = model.generate(inputs['input_ids'], max_length=max_length, temperature=temperature) | |
| # return tokenizer.decode(output[0], skip_special_tokens=True) | |
| # # Create the Gradio interface | |
| # iface = gr.Interface( | |
| # fn=generate_text, | |
| # inputs=[ | |
| # gr.Textbox(label="Enter your prompt", placeholder="Start typing..."), | |
| # gr.Slider(minimum=50, maximum=200, label="Max Length", value=100), | |
| # gr.Slider(minimum=0.1, maximum=1.0, label="Temperature", value=0.7), | |
| # ], | |
| # outputs="text", | |
| # title="LLaMA 3.2 Text Generator", | |
| # description="Enter a prompt to generate text using the LLaMA 3.2 model.", | |
| # ) | |
| # # Launch the Gradio app | |
| # iface.launch() | |