import gradio as gr from llama_cpp import Llama from huggingface_hub import hf_hub_download def load_model(): repo_id = "forestav/gguf_lora_model" model_file = "unsloth.F16.gguf" local_path = hf_hub_download( repo_id=repo_id, filename=model_file ) print(f"Loading model from: {local_path}") model = Llama( model_path=local_path, n_ctx=2048, n_threads=8 ) return model def generate_career_response(message, history): # Enhance the prompt with career guidance context enhanced_prompt = f"""As a career development advisor, help the user with their professional growth. Consider: 1. Skill development opportunities 2. Industry trends 3. Practical next steps 4. Resources and learning paths User Query: {message} Provide a structured response with actionable advice.""" response = model.create_chat_completion( messages=[ {"role": "system", "content": "You are a professional career advisor focused on providing practical, actionable guidance for career development."}, {"role": "user", "content": enhanced_prompt} ], max_tokens=512, temperature=0.7, top_p=0.95, ) return response['choices'][0]['message']['content'] # Load model globally print("Starting model loading...") model = load_model() print("Model loaded successfully!") # Create Gradio interface with career-focused examples demo = gr.ChatInterface( fn=generate_career_response, title="Career Growth Navigator 🚀", description="""Your AI career development partner. Ask about: • Skill development paths • Career transition strategies • Industry trends and opportunities • Resume and interview preparation • Professional networking advice • Work-life balance Let's shape your professional future together!""", examples=[ "I'm a software developer wanting to transition into AI/ML. What skills should I focus on?", "How can I improve my leadership skills in my current role?", "What are the key trends in digital marketing I should be aware of?", "I want to start freelancing in web development. Where should I begin?", "How can I negotiate a promotion in my current position?" ] ) # Add proper Gradio launch configuration for Spaces demo.launch( server_name="0.0.0.0", server_port=7860, share=False )