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Create app.py
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app.py
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import requests
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import streamlit as st
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from sentence_transformers import SentenceTransformer, util
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# Load embedding model
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@st.cache_resource
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def load_model():
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return SentenceTransformer("all-MiniLM-L6-v2")
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model = load_model()
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# Fetch jobs from RemoteOK
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def fetch_jobs():
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url = "https://remoteok.com/api"
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try:
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response = requests.get(url, headers={"User-Agent": "Mozilla/5.0"})
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data = response.json()
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jobs = data[1:15] # skip metadata, get first 15 jobs
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return jobs
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except Exception as e:
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st.error(f"Error fetching jobs: {e}")
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return []
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# Match jobs with user skills
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def match_jobs(user_skills):
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jobs = fetch_jobs()
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if not jobs:
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return []
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user_embedding = model.encode(user_skills, convert_to_tensor=True)
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results = []
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for job in jobs:
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desc = f"{job.get('position','')} at {job.get('company','')}. Tags: {job.get('tags','')}"
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job_embedding = model.encode(desc, convert_to_tensor=True)
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similarity = util.cos_sim(user_embedding, job_embedding).item()
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results.append({
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"company": job.get("company"),
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"position": job.get("position"),
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"url": job.get("url"),
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"score": round(similarity * 100, 2)
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})
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results = sorted(results, key=lambda x: x["score"], reverse=True)
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return results[:5]
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# ---------------- UI ----------------
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st.set_page_config(page_title="AI Freelancer Job Matcher", page_icon="🤖", layout="wide")
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st.title("🤖 AI Freelancer Job Matcher")
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st.markdown("Find jobs in real-time from **RemoteOK**, matched to your skills using AI.")
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skills = st.text_input("Enter your skills (comma separated)", "Python, AI, Docker")
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if st.button("Find Jobs"):
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results = match_jobs(skills)
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if results:
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for job in results:
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st.subheader(f"🔹 {job['position']} at {job['company']}")
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st.write(f"**Match:** {job['score']}%")
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st.markdown(f"[Apply here]({job['url']})")
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st.markdown("---")
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else:
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st.warning("No jobs found. Try again later.")
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