Update app.py

app.py

@@ -1,88 +1,105 @@
 """
 AI Resume Studio – Hugging Face Space
 Author: Oluwafemi Idiakhoa
-Last update: 2025-06-27
+Updated: 2025-06-27
 
 Features
 ────────
-1. Generate resume → Word & PDF downloads
-2. Score resume against job description
+1. Generate résumé → Word & PDF downloads
+2. Score résumé vs. job description
 3. AI Section Co-Pilot (rewrite, quantify, bulletize…)
 4. Cover-letter generator
-5. Job-description scraper by URL
+5. Job-description via LinkedIn API (OAuth client_credentials)
 6. Multilingual export via Deep-Translator (DeepL backend)
 """
 
-import os
-import tempfile
+import os, re, tempfile
 import requests
 import gradio as gr
 import google.generativeai as genai
 from dotenv import load_dotenv
-from bs4 import BeautifulSoup
-from docx import Document            # python-docx
+from docx import Document
 from reportlab.lib.pagesizes import LETTER
 from reportlab.pdfgen import canvas
 from deep_translator import DeeplTranslator
 
 # ──────────────────────────────────────────────────────────────────────────────
-# Environment & Model Setup
+# Load secrets & configure services
 # ──────────────────────────────────────────────────────────────────────────────
 load_dotenv()
 
-# Gemini (DeepMind) setup
-genai.configure(api_key=os.getenv("API_KEY"))
+# Gemini setup
+genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
 GEMINI = genai.GenerativeModel("gemini-1.5-pro-latest")
 
 # DeepL via Deep-Translator
 DEEPL_KEY = os.getenv("DEEPL_API_KEY")
-# Note: Deep-Translator’s DeeplTranslator requires target_lang codes like "FR", "DE", etc.
-def translate_text(text: str, target_code: str) -> str:
-    if not DEEPL_KEY or target_code == "EN":
-        return text
+def translate_text(text: str, tgt: str) -> str:
+    if not DEEPL_KEY or tgt.upper()=="EN": return text
     try:
-        return DeeplTranslator(api_key=DEEPL_KEY, target=target_code).translate(text)
+        return DeeplTranslator(api_key=DEEPL_KEY, target=tgt).translate(text)
     except Exception as e:
         return f"[Translation Error] {e}\n\n{text}"
 
-# Supported languages mapping
-LANGS = {
-    "EN": "English",
-    "DE": "German",
-    "FR": "French",
-    "ES": "Spanish",
-    "IT": "Italian",
-    "NL": "Dutch",
-    "PT": "Portuguese",
-    "PL": "Polish",
-    "JA": "Japanese",
-    "ZH": "Chinese",
-}
+# LinkedIn OAuth 2.0 Client-Credentials
+LINKEDIN_CLIENT_ID     = os.getenv("LINKEDIN_CLIENT_ID")
+LINKEDIN_CLIENT_SECRET = os.getenv("LINKEDIN_CLIENT_SECRET")
+_TOKEN_CACHE = {}
+def get_linkedin_token():
+    # cache until expiry
+    token_data = _TOKEN_CACHE.get("data")
+    if token_data and token_data["expires_at"] > time.time():
+        return token_data["access_token"]
+    resp = requests.post(
+        "https://www.linkedin.com/oauth/v2/accessToken",
+        data={
+            "grant_type":    "client_credentials",
+            "client_id":     LINKEDIN_CLIENT_ID,
+            "client_secret": LINKEDIN_CLIENT_SECRET,
+        },
+    )
+    resp.raise_for_status()
+    data = resp.json()
+    data["expires_at"] = time.time() + data.get("expires_in", 0) - 60
+    _TOKEN_CACHE["data"] = data
+    return data["access_token"]
+
+def fetch_job_via_api(job_url: str) -> str:
+    job_id = (re.search(r"/jobs/view/(\d+)", job_url) or re.search(r"currentJobId=(\d+)", job_url))
+    if not job_id:
+        return "[Error] Unable to parse job ID from URL."
+    token = get_linkedin_token()
+    headers = {"Authorization": f"Bearer {token}"}
+    # LinkedIn v2 Jobs endpoint (requires r_jobs scope)
+    api_url = f"https://api.linkedin.com/v2/jobPosts/{job_id.group(1)}?projection=(description)"
+    r = requests.get(api_url, headers=headers, timeout=10)
+    if r.status_code != 200:
+        return f"[LinkedIn API Error {r.status_code}] {r.text}"
+    return r.json().get("description", "")
 
 # ──────────────────────────────────────────────────────────────────────────────
-# AI / File Helpers
+# AI & File utilities
 # ──────────────────────────────────────────────────────────────────────────────
 def ask_gemini(prompt: str, temp: float = 0.6) -> str:
     try:
-        res = GEMINI.generate_content(prompt, generation_config={"temperature": temp})
-        return res.text.strip()
+        return GEMINI.generate_content(prompt, generation_config={"temperature": temp}).text.strip()
     except Exception as e:
         return f"[Gemini Error] {e}"
 
-def save_docx(content: str) -> str:
+def save_docx(text: str) -> str:
     f = tempfile.NamedTemporaryFile(delete=False, suffix=".docx")
     doc = Document()
-    for line in content.splitlines():
+    for line in text.splitlines():
         doc.add_paragraph(line)
     doc.save(f.name)
     return f.name
 
-def save_pdf(content: str) -> str:
+def save_pdf(text: str) -> str:
     f = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
     c = canvas.Canvas(f.name, pagesize=LETTER)
     width, height = LETTER
     y = height - 72
-    for line in content.splitlines():
+    for line in text.splitlines():
         c.drawString(72, y, line)
         y -= 14
         if y < 72:
@@ -92,18 +109,23 @@ def save_pdf(content: str) -> str:
     return f.name
 
 # ──────────────────────────────────────────────────────────────────────────────
-# Core Features
+# Core application logic
 # ──────────────────────────────────────────────────────────────────────────────
-def generate_resume(name, email, phone, summary, exp, edu, skills, lang_code):
+LANGS = {
+    "EN": "English", "DE": "German",  "FR": "French",   "ES": "Spanish",
+    "IT": "Italian", "NL": "Dutch",   "PT": "Portuguese","PL": "Polish",
+    "JA": "Japanese","ZH": "Chinese"
+}
+
+def generate_resume(name, email, phone, summary, exp, edu, skills, lang):
     prompt = f"""
-Create a professional résumé in Markdown without first-person pronouns.
-Output language: {LANGS[lang_code]}
+Create a professional résumé in Markdown—no first-person. Output in {LANGS[lang]}.
 
 Name: {name}
 Email: {email}
 Phone: {phone}
 
-Professional Summary:
+Summary:
 {summary}
 
 Experience:
@@ -116,35 +138,32 @@ Skills:
 {skills}
 """
     md = ask_gemini(prompt)
-    return translate_text(md, lang_code)
+    return translate_text(md, lang)
+
+def generate_and_export(name, email, phone, summary, exp, edu, skills, lang):
+    md = generate_resume(name, email, phone, summary, exp, edu, skills, lang)
+    return md, save_docx(md), save_pdf(md)
 
 def score_resume(resume_md, job_desc):
     prompt = f"""
-Evaluate this résumé against the job description. Return compact Markdown:
+Evaluate this résumé vs. the job description. Return Markdown:
 
 ### Match Score
 <0-100>
 
 ### Suggestions
-- suggestion 1
-- suggestion 2
+- ...
 """
     return ask_gemini(prompt, temp=0.4)
 
-def refine_section(section, instruction, lang_code):
-    prompt = f"""
-Apply the following instruction to this résumé section. Respond in {LANGS[lang_code]}.
-
-Instruction: {instruction}
-Section:
-{section}
-"""
+def refine_section(section, instr, lang):
+    prompt = f"Refine this résumé section in {LANGS[lang]}.\nInstruction: {instr}\nText:\n{section}"
     out = ask_gemini(prompt)
-    return translate_text(out, lang_code)
+    return translate_text(out, lang)
 
-def generate_cover_letter(resume_md, job_desc, tone, lang_code):
+def generate_cover_letter(resume_md, job_desc, tone, lang):
     prompt = f"""
-Draft a one-page cover letter (max 300 words) in a {tone} tone, matching this résumé to the job description. Use {LANGS[lang_code]}.
+Draft a one-page cover letter (≤300 words), {tone} tone, in {LANGS[lang]}.
 Salutation: "Dear Hiring Manager,"
 
 Résumé:
@@ -154,88 +173,67 @@ Job Description:
 {job_desc}
 """
     letter = ask_gemini(prompt)
-    return translate_text(letter, lang_code)
-
-def scrape_job_description(url):
-    try:
-        hdr = {"User-Agent": "Mozilla/5.0"}
-        r = requests.get(url, headers=hdr, timeout=10)
-        soup = BeautifulSoup(r.text, "html.parser")
-        for sel in ["div.jobsearch-jobDescriptionText", "section.description", "div.jobs-description__content"]:
-            block = soup.select_one(sel)
-            if block:
-                return block.get_text(" ", strip=True)
-        return soup.get_text(" ", strip=True)[:3000]
-    except Exception as e:
-        return f"[Scrape Error] {e}"
-
-def generate_and_export(name, email, phone, summary, exp, edu, skills, lang_code):
-    md = generate_resume(name, email, phone, summary, exp, edu, skills, lang_code)
-    return md, save_docx(md), save_pdf(md)
+    return translate_text(letter, lang)
 
 # ──────────────────────────────────────────────────────────────────────────────
 # Gradio UI
 # ──────────────────────────────────────────────────────────────────────────────
 with gr.Blocks(title="AI Resume Studio") as demo:
-    gr.Markdown("## 🧠 Interactive AI Resume Studio (Gemini × DeepL)")
+    gr.Markdown("## 🧠 AI Resume Studio (Gemini × DeepL × LinkedIn)")
 
-    # Generate Resume
-    with gr.Tab("📄 Generate Resume"):
+    # Tab 1: Generate Résumé
+    with gr.Tab("📄 Generate Résumé"):
         with gr.Row():
             name_in, email_in, phone_in = gr.Textbox(label="Name"), gr.Textbox(label="Email"), gr.Textbox(label="Phone")
-        summary_in    = gr.Textbox(label="Professional Summary")
-        exp_in        = gr.Textbox(label="Experience")
-        edu_in        = gr.Textbox(label="Education")
-        skills_in     = gr.Textbox(label="Skills")
-        lang_in       = gr.Dropdown(list(LANGS.keys()), value="EN", label="Language")
+        sum_in    = gr.Textbox(label="Summary")
+        exp_in    = gr.Textbox(label="Experience")
+        edu_in    = gr.Textbox(label="Education")
+        skills_in = gr.Textbox(label="Skills")
+        lang_in   = gr.Dropdown(list(LANGS.keys()), value="EN", label="Language")
 
-        resume_md_out = gr.Markdown(label="Résume (Markdown)")
-        docx_out      = gr.File(label="⬇ Download .docx")
-        pdf_out       = gr.File(label="⬇ Download .pdf")
-        gen_btn       = gr.Button("Generate")
+        out_md     = gr.Markdown(label="Résumé (Markdown)")
+        out_docx   = gr.File(label="⬇ Download .docx")
+        out_pdf    = gr.File(label="⬇ Download .pdf")
+        gen_btn    = gr.Button("Generate")
 
         gen_btn.click(
             generate_and_export,
-            inputs=[name_in, email_in, phone_in, summary_in, exp_in, edu_in, skills_in, lang_in],
-            outputs=[resume_md_out, docx_out, pdf_out],
+            inputs=[name_in, email_in, phone_in, sum_in, exp_in, edu_in, skills_in, lang_in],
+            outputs=[out_md, out_docx, out_pdf],
         )
 
-    # Score Resume
-    with gr.Tab("🧮 Score Resume Against Job"):
-        resume_score_in = gr.Textbox(label="Résumé (Markdown)", lines=10)
-        jd_score_in     = gr.Textbox(label="Job Description", lines=8)
-        score_out       = gr.Markdown(label="Score & Suggestions")
-        score_btn       = gr.Button("Evaluate")
-
-        score_btn.click(score_resume, inputs=[resume_score_in, jd_score_in], outputs=score_out)
+    # Tab 2: Score Résumé
+    with gr.Tab("🧮 Score Résumé Against Job"):
+        res_in  = gr.Textbox(label="Résumé (Markdown)", lines=10)
+        jd_in   = gr.Textbox(label="Job Description",   lines=8)
+        score_out = gr.Markdown(label="Score & Suggestions")
+        score_btn = gr.Button("Evaluate")
+        score_btn.click(score_resume, inputs=[res_in, jd_in], outputs=score_out)
 
-    # AI Section Co-Pilot
+    # Tab 3: AI Section Co-Pilot
     with gr.Tab("✏️ AI Section Co-Pilot"):
-        sec_in    = gr.Textbox(label="Section Text", lines=6)
-        inst_in   = gr.Radio(["Rewrite", "Make More Concise", "Quantify Achievements", "Convert to Bullet Points"], label="Action")
-        lang_sec  = gr.Dropdown(list(LANGS.keys()), value="EN", label="Language")
-        sec_out   = gr.Textbox(label="AI Output", lines=6)
-        sec_btn   = gr.Button("Apply")
-
-        sec_btn.click(refine_section, inputs=[sec_in, inst_in, lang_sec], outputs=sec_out)
-
-    # Cover-Letter Generator
+        sec_in   = gr.Textbox(label="Section Text", lines=6)
+        action   = gr.Radio(["Rewrite","Make More Concise","Quantify Achievements","Convert to Bullet Points"], label="Action")
+        sec_lang = gr.Dropdown(list(LANGS.keys()), value="EN", label="Language")
+        sec_out  = gr.Textbox(label="AI Output", lines=6)
+        sec_btn  = gr.Button("Apply")
+        sec_btn.click(refine_section, inputs=[sec_in, action, sec_lang], outputs=sec_out)
+
+    # Tab 4: Cover-Letter Generator
     with gr.Tab("📧 Cover-Letter Generator"):
-        cv_res_in = gr.Textbox(label="Résumé (Markdown)", lines=12)
-        cv_jd_in  = gr.Textbox(label="Job Description", lines=8)
-        cv_tone   = gr.Radio(["Professional", "Friendly", "Enthusiastic"], value="Professional", label="Tone")
-        cv_lang   = gr.Dropdown(list(LANGS.keys()), value="EN", label="Language")
-        cv_out    = gr.Markdown(label="Cover Letter")
-        cv_btn    = gr.Button("Generate")
-
-        cv_btn.click(generate_cover_letter, inputs=[cv_res_in, cv_jd_in, cv_tone, cv_lang], outputs=cv_out)
-
-    # Job-Description Scraper
-    with gr.Tab("🌐 Job Description Scraper"):
-        url_in    = gr.Textbox(label="Job URL")
-        jd_out    = gr.Textbox(label="Extracted Description", lines=12)
-        scrape_btn= gr.Button("Fetch")
-
-        scrape_btn.click(scrape_job_description, inputs=[url_in], outputs=[jd_out])
+        cv_res  = gr.Textbox(label="Résumé (Markdown)", lines=12)
+        cv_jd   = gr.Textbox(label="Job Description",   lines=8)
+        cv_tone = gr.Radio(["Professional","Friendly","Enthusiastic"], label="Tone")
+        cv_lang = gr.Dropdown(list(LANGS.keys()), value="EN", label="Language")
+        cv_out  = gr.Markdown(label="Cover Letter")
+        cv_btn  = gr.Button("Generate")
+        cv_btn.click(generate_cover_letter, inputs=[cv_res, cv_jd, cv_tone, cv_lang], outputs=cv_out)
+
+    # Tab 5: LinkedIn Job Fetcher
+    with gr.Tab("🌐 Fetch Job via LinkedIn API"):
+        url_in    = gr.Textbox(label="LinkedIn Job URL")
+        jd_out    = gr.Textbox(label="Job Description", lines=12)
+        fetch_btn = gr.Button("Fetch from LinkedIn")
+        fetch_btn.click(fetch_job_via_api, inputs=[url_in], outputs=[jd_out])
 
 demo.launch(share=False)