import gradio as gr from transformers import AutoTokenizer, AutoModelForCausalLM from datetime import datetime, timezone # Initialize the model and tokenizer model_id = "CohereLabs/c4ai-command-a-03-2025" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) def get_timestamp(): """Get current UTC datetime in specified format""" return datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M:%SS') def format_system_info(): """Format system information header""" return ( f"Current Date and Time (UTC - YYYY-MM-DD HH:MM:SS formatted): {get_timestamp()}\n" f"Current User's Login: Raj-VedAI\n" ) def chat(message, history): if history is None: history = [] # Add system information system_info = format_system_info() # Format messages with the c4ai-command-a-03-2025 chat template messages = [{"role": "user", "content": message}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True) # Generate response gen_tokens = model.generate( input_ids, max_new_tokens=100, do_sample=True, temperature=0.3, ) # Decode response gen_text = tokenizer.decode(gen_tokens[0]) # Format the full response with system info formatted_response = f"{system_info}\n{gen_text}" history.append((message, formatted_response)) return history # Create custom theme theme = gr.themes.Default().set( body_background_fill="#f0f8ff", block_background_fill="#ffffff", block_border_width="1px", block_border_color="#2c3e50", block_radius="10px" ) # Create the Gradio interface demo = gr.ChatInterface( fn=chat, title="Medical Decision Support AI", description="""A medical decision support system that provides healthcare-related information and guidance. Current UTC Time: """ + get_timestamp(), theme=theme, examples=[ "What are the symptoms of hypertension?", "What are common drug interactions with aspirin?", "What are the warning signs of diabetes?", ], retry_on_error=True ) demo.launch()