Create app.py

app.py

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+import streamlit as st
+import json
+import torch
+from inference import infer_from_text
+
+# GPU check
+if torch.cuda.is_available():
+    st.info(f" GPU is available: {torch.cuda.get_device_name(0)}")
+else:
+    st.warning(" GPU is NOT available. Running on CPU.")
+
+# Page config
+st.set_page_config(
+    page_title="Job Description Parser Demo",
+    page_icon="📝",
+    layout="wide"
+)
+
+# Title
+st.markdown("## 📝 Job Description Parser Demo")
+
+# Sample job descriptions
+sample_jds = {
+    " Machine Learning Engineer Example": """Job Title: Machine Learning Engineer 
+About the Role:
+At ZentrixAI, we're redefining how data-driven intelligence powers products in healthcare and insurance.
+ We're looking for a Machine Learning Engineer to build, train, and optimize models that turn messy real-world data into actionable insights. 
+ If you love solving complex problems, deploying scalable ML pipelines, and shipping features that matter, you'll thrive here.
+
+Responsibilities:
+
+Design and develop machine learning models for NLP, tabular prediction, and anomaly detection.
+
+Preprocess and normalize large-scale structured and unstructured datasets.
+
+Collaborate with MLOps to deploy models into production (TensorFlow Serving / TorchServe).
+
+Evaluate model performance using AUC, precision-recall, F1, etc.
+
+Work closely with Data Engineers and Product Managers to define model goals.
+
+Continuously improve models using online learning and feedback loops.
+
+Write scalable training and inference code using TensorFlow and PyTorch.
+
+Maintain model versioning using MLflow and integrate with CI/CD pipelines.
+
+Technical Skills:
+
+Python (NumPy, Pandas, Scikit-learn)
+
+TensorFlow, PyTorch, Keras
+
+MLflow, Docker, FastAPI
+
+SQL, Spark
+
+Cloud ML tools (GCP AI Platform, AWS SageMaker)
+
+NLP libraries (spaCy, Transformers, NLTK)
+
+Git, GitHub Actions, Kubernetes basics
+
+Soft Skills:
+
+Team collaboration
+
+Curiosity and continuous learning
+
+Communication with non-tech stakeholders
+
+Time prioritization
+
+Initiative-taking mindset
+
+Qualifications:
+
+Bachelor's degree in Computer Science, AI, Data Science, or similar
+
+Preferred: Master's in Machine Learning or Applied Mathematics
+
+Certifications:
+
+TensorFlow Developer Certificate
+
+AWS Certified Machine Learning - Specialty
+
+Languages:
+
+English (Fluent)
+
+Mandarin (Basic)
+
+Compensation & Benefits:
+
+Salary: SGD 7,500 - SGD 10,000 per month
+
+Time Frequency: Monthly
+
+Benefits: Remote work setup budget, flexible hours, learning allowance, stock grants, health insurance
+
+Employment Details:
+
+Full-time
+
+Remote (preferably working in Singapore Standard Time)
+
+Location:
+
+Hiring: Remote (Singapore time zone overlap)
+
+Org Location: Singapore
+
+Contact Info:
+
+Email: jobs@zentrixai.com
+
+Phone: +65 6904 8899
+
+Website: https://www.zentrixai.com/careers
+
+About ZentrixAI:
+ZentrixAI is an award-winning AI-first company focused on transforming decision-making for insurers and hospitals through intelligent automation. 
+With a growing international team, we blend academic rigor with product agility.
+"""
+}
+
+# Input section
+selected = st.selectbox(
+    "Select a sample JD to auto-fill the text area", 
+    [""] + list(sample_jds.keys())
+)
+jd_text = st.text_area(
+    "Job Description:", 
+    value=sample_jds.get(selected, ""), 
+    height=300
+)
+
+# Parse button and output
+if st.button("⚡ Click here to Parse") and jd_text.strip():
+    try:
+        with st.spinner("Parsing job description..."):
+            parsed_output, duration = infer_from_text(jd_text)
+        st.success(f"✅ Parsed in {duration} seconds")
+        # Try to parse and display as JSON
+        try:
+            parsed_json = json.loads(parsed_output)
+            st.json(parsed_json)
+            st.download_button(
+                "📋 Download JSON",
+                json.dumps(parsed_json, indent=2),
+                file_name="parsed_jd.json",
+                mime="application/json"
+            )
+        except Exception:
+            st.error("Could not parse output as JSON. Showing raw output:")
+            st.code(parsed_output, language="text")
+    except Exception as e:
+        st.error(f"Error during parsing: {str(e)}")