""" Job Description Generator - Streamlit App """ import streamlit as st import torch from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig from peft import PeftModel import time # Page config st.set_page_config( page_title="Job Description Generator", page_icon="💼", layout="wide" ) # Title st.title("💼 AI Job Description Generator") st.markdown("### Powered by LLaMA-3 8B Fine-tuned on 15,885 Job Postings") # Sidebar with st.sidebar: st.header("⚙️ Generation Settings") temperature = st.slider("Temperature", 0.1, 1.5, 0.7, 0.1) top_k = st.slider("Top-K", 10, 100, 50, 10) top_p = st.slider("Top-P", 0.5, 1.0, 0.95, 0.05) max_length = st.slider("Max Length", 200, 800, 400, 50) st.markdown("---") st.info(""" **Model**: LLaMA-3 8B **Method**: QLoRA (4-bit) **Training**: 15,885 samples **Performance**: +244% BLEU """) if st.button("🗑️ Clear History"): st.session_state.messages = [] st.rerun() # Initialize session state if "messages" not in st.session_state: st.session_state.messages = [] if "model" not in st.session_state: st.session_state.model = None # Load model @st.cache_resource def load_model(): with st.spinner("🔄 Loading model... (2-3 minutes first time)"): try: model_id = "zewial/job-description-generator-llama3-8b" base_model = "meta-llama/Meta-Llama-3-8B-Instruct" bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True ) tokenizer = AutoTokenizer.from_pretrained(model_id) if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token tokenizer.pad_token_id = tokenizer.eos_token_id model = AutoModelForCausalLM.from_pretrained( base_model, quantization_config=bnb_config, device_map="auto", trust_remote_code=True ) model = PeftModel.from_pretrained(model, model_id) model.eval() return model, tokenizer except Exception as e: st.error(f"❌ Error loading model: {str(e)}") st.info("💡 Make sure model is uploaded at: https://huggingface.co/zewial/job-description-generator-llama3-8b") return None, None # Load model if not loaded if st.session_state.model is None: model, tokenizer = load_model() if model is not None: st.session_state.model = model st.session_state.tokenizer = tokenizer st.success("✅ Model loaded successfully!") else: model = st.session_state.model tokenizer = st.session_state.tokenizer # Main interface if model is not None: # Input form with st.form("job_form", clear_on_submit=True): st.subheader("📝 Job Details") col1, col2 = st.columns(2) with col1: job_title = st.text_input( "Job Title *", placeholder="e.g., Senior Data Scientist", help="The position you're hiring for" ) work_type = st.selectbox( "Work Type *", ["Full-time", "Part-time", "Contract", "Internship", "Remote"], help="Employment type" ) with col2: experience = st.selectbox( "Experience Level *", ["Entry level", "Mid-Senior level", "Senior level", "Executive level"], index=1, help="Required experience level" ) location = st.text_input( "Location *", placeholder="e.g., San Francisco, CA or Remote", help="Job location" ) additional = st.text_area( "Additional Instructions (Optional)", placeholder="Any specific requirements, benefits, or details to include...", help="Optional: Add specific details you want in the description" ) submitted = st.form_submit_button("🚀 Generate Job Description", use_container_width=True) # Generate when submitted if submitted: if not job_title or not location: st.error("❌ Please fill in Job Title and Location") else: # Create prompt prompt = f"""<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are an expert HR professional who writes clear, professional, and comprehensive job descriptions.<|eot_id|><|start_header_id|>user<|end_header_id|> Generate a professional job description for: Job Title: {job_title} Experience Level: {experience} Work Type: {work_type} Location: {location}""" if additional: prompt += f"\n\nAdditional Requirements:\n{additional}" prompt += "\n\nCreate a comprehensive job description with company/role overview, responsibilities, qualifications, and benefits.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" # Store user input user_msg = f"**Title:** {job_title} | **Level:** {experience} | **Type:** {work_type} | **Location:** {location}" st.session_state.messages.append({"role": "user", "content": user_msg}) # Generate with st.spinner("✨ Generating job description..."): try: inputs = tokenizer( prompt, return_tensors="pt", max_length=512, truncation=True ).to(model.device) start_time = time.time() with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=max_length, temperature=temperature, top_k=top_k, top_p=top_p, repetition_penalty=1.1, do_sample=True, pad_token_id=tokenizer.pad_token_id ) gen_time = time.time() - start_time generated = tokenizer.decode( outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True ) # Store assistant response st.session_state.messages.append({ "role": "assistant", "content": generated, "time": gen_time, "tokens": len(outputs[0]) - inputs['input_ids'].shape[1], "title": job_title }) except Exception as e: st.error(f"❌ Generation error: {str(e)}") # Display chat history if len(st.session_state.messages) > 0: st.markdown("---") st.subheader("💬 Generation History") for idx, msg in enumerate(st.session_state.messages): if msg["role"] == "user": with st.container(): st.markdown(f"**👤 Your Input:**") st.info(msg["content"]) else: with st.container(): st.markdown(f"**🤖 Generated Description:**") st.success(msg["content"]) # Stats col1, col2, col3 = st.columns(3) with col1: st.metric("⏱️ Time", f"{msg.get('time', 0):.1f}s") with col2: st.metric("📝 Words", len(msg["content"].split())) with col3: st.metric("🔤 Tokens", msg.get('tokens', 0)) # Download button st.download_button( label="📥 Download as Text", data=msg["content"], file_name=f"job_description_{msg.get('title', 'output').replace(' ', '_')}.txt", mime="text/plain", key=f"download_{idx}" ) st.markdown("---") if len(st.session_state.messages) == 0: st.info("👆 Fill in the form above and click 'Generate Job Description' to start!") else: st.error("❌ Model failed to load. Please check the logs or contact support.") # Footer st.markdown("---") st.markdown("""

Built with ❤️ using LLaMA-3 8B | Fine-tuned on 15,885 job postings

⚡ Powered by QLoRA | 🚀 Deployed on Hugging Face Spaces

""", unsafe_allow_html=True)