Initial hackathon demo

.gitignore

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+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv
+uv.lock
+.env

.python-version

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+3.12

app.py

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+# app.py
+
+import os, json
+from dotenv import load_dotenv
+from openai import OpenAI
+from pypdf import PdfReader
+import gradio as gr
+import re, json
+
+def extract_json(text: str) -> str:
+    """(You already have this) Strip ``` fences so we can parse JSON."""
+    text = re.sub(r"```(?:json)?\s*", "", text)
+    text = re.sub(r"\s*```", "", text)
+    start = text.find("{"); end = text.rfind("}")
+    return text[start:end+1] if start!=-1 and end!=-1 else text
+
+def format_results_as_md(res: dict) -> str:
+    """Turn the parsed dict into a nice Markdown string."""
+    if "error" in res:
+        return f"**Error:** {res['error']}\n\n```\n{res.get('raw','')}\n```"
+
+    md = ["## 🎯 Top Skill Gaps"]
+    for gap in res.get("skill_gaps", []):
+        md.append(f"- {gap}")
+
+    md.append("\n## 📚 Course Recommendations")
+    for course in res.get("course_recommendations", []):
+        name = course.get("course_name")
+        provider = course.get("provider")
+        link = course.get("link")
+        md.append(f"- [{name}]({link})  _(Provider: {provider})_")
+
+    md.append("\n## 🏅 Certification Recommendations")
+    for cert in res.get("certification_recommendations", []):
+        md.append(f"- {cert}")
+
+    return "\n".join(md)
+
+# ─── 1) Load keys ───────────────────────────────────────────────────────────────
+
+load_dotenv()            # expects OPENAI_API_KEY in your .env
+openai = OpenAI()
+
+# ─── 2) Helpers ─────────────────────────────────────────────────────────────────
+
+def read_resume(file_obj):
+    """Extract text from a PDF or return raw text."""
+    name = file_obj.name.lower()
+    if name.endswith(".pdf"):
+        reader = PdfReader(file_obj.name)
+        return "\n".join(page.extract_text() or "" for page in reader.pages)
+    else:
+        return file_obj.read().decode("utf-8")
+
+def analyze_skills_and_recs(resume_text, titles_list):
+    prompt = {
+        "role":"user",
+        "content":(
+            "You are an expert career coach.\n\n"
+            "Here is a candidate's resume (first 2k chars):\n```\n"
+            + resume_text[:2000] + "\n```\n\n"
+            "They want to apply for these roles:\n  • "
+            + "\n  • ".join(titles_list) + "\n\n"
+            "Please reply _only_ in JSON with these fields:\n"
+            '  "skill_gaps": [ "…" ],\n'
+            '  "course_recommendations": [\n'
+            '      { "course_name": "…", "provider": "…", "link": "…" }\n'
+            '  ],\n'
+            '  "certification_recommendations": [ "…" ]\n'
+        )
+    }
+    resp = openai.chat.completions.create(
+        model="gpt-4o-mini",
+        messages=[prompt]
+    )
+    raw = resp.choices[0].message.content
+    cleaned = extract_json(raw)
+    try:
+        data = json.loads(cleaned)
+    except json.JSONDecodeError:
+        return {
+            "error": "Still could not parse after stripping fences",
+            "raw": raw
+        }
+    return format_results_as_md(data)
+
+# ─── 3) Pipeline ────────────────────────────────────────────────────────────────
+
+def run_agent(resume_file, titles_str):
+    resume = read_resume(resume_file)
+    titles = [t.strip() for t in titles_str.split(",") if t.strip()]
+    if not titles:
+        return {"error": "Please enter at least one job title."}
+    return analyze_skills_and_recs(resume, titles)
+
+# ─── 4) Gradio UI ───────────────────────────────────────────────────────────────
+
+app = gr.Blocks()
+
+with app:
+    gr.Markdown("## 🎓 Skill Gap & Course Advisor")
+    gr.Markdown(
+        "Upload your resume, enter the roles you want (comma-separated), "
+        "and get back:\n"
+        "1. Your top skill gaps\n"
+        "2. 5 course recommendations (with names, providers, links)\n"
+        "3. 3 certification recommendations"
+    )
+
+    with gr.Row():
+        resume_input = gr.File(label="📄 Resume (PDF or TXT)")
+        titles_input = gr.Textbox(
+            label="🎯 Target Job Titles",
+            placeholder="e.g. Data Scientist, ML Engineer"
+        )
+
+    run_btn = gr.Button("🚀 Analyze & Recommend")
+    output = gr.Markdown(label="📋 Recommendations")
+    run_btn.click(run_agent, [resume_input, titles_input], output)
+
+if __name__ == "__main__":
+    # # Local:
+    # app.launch(server_name="0.0.0.0", server_port=7860, debug=True)
+    # For hackathon submission (MCP mode):
+    app.launch(mcp_server=True, server_name="0.0.0.0", server_port=7860)

pyproject.toml

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+[project]
+name = "hackathon-job-agent"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "gradio>=5.33.0",
+    "openai>=1.84.0",
+    "pypdf>=5.6.0",
+    "python-dotenv>=1.1.0",
+]

requirements.txt

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+gradio
+openai
+pypdf
+python-dotenv
+requests