Update app.py

app.py

@@ -9,76 +9,107 @@ Features
 2. Score résumé vs. job description
 3. AI Section Co-Pilot (rewrite, quantify, bulletize…)
 4. Cover-letter generator
-5. Job-description via LinkedIn API (OAuth client_credentials)
+5. Job-description via LinkedIn API (OAuth client_credentials) + fallback scraping
 6. Multilingual export via Deep-Translator (DeepL backend)
 """
 
-import os, re, tempfile
+import os
+import re
+import time
+import 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
 from reportlab.lib.pagesizes import LETTER
 from reportlab.pdfgen import canvas
 from deep_translator import DeeplTranslator
 
 # ──────────────────────────────────────────────────────────────────────────────
-# Load secrets & configure services
+# Load Secrets & Configure Clients
 # ──────────────────────────────────────────────────────────────────────────────
 load_dotenv()
 
-# Gemini setup
-genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
+# Gemini
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+genai.configure(api_key=GEMINI_API_KEY)
 GEMINI = genai.GenerativeModel("gemini-1.5-pro-latest")
 
 # DeepL via Deep-Translator
 DEEPL_KEY = os.getenv("DEEPL_API_KEY")
 def translate_text(text: str, tgt: str) -> str:
-    if not DEEPL_KEY or tgt.upper()=="EN": return text
+    if not DEEPL_KEY or tgt.upper() == "EN":
+        return text
     try:
         return DeeplTranslator(api_key=DEEPL_KEY, target=tgt).translate(text)
     except Exception as e:
         return f"[Translation Error] {e}\n\n{text}"
 
-# LinkedIn OAuth 2.0 Client-Credentials
-LINKEDIN_CLIENT_ID     = os.getenv("LINKEDIN_CLIENT_ID")
-LINKEDIN_CLIENT_SECRET = os.getenv("LINKEDIN_CLIENT_SECRET")
-_TOKEN_CACHE = {}
+# LinkedIn OAuth 2.0 (Client Credentials)
+CLIENT_ID     = os.getenv("LINKEDIN_CLIENT_ID")
+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"]
+    data = _token_cache.get("data", {})
+    if data and data.get("expires_at", 0) > time.time():
+        return 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,
+            "client_id":     CLIENT_ID,
+            "client_secret": CLIENT_SECRET,
         },
+        timeout=10
     )
-    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:
+    if resp.status_code != 200:
+        # raise or let caller fallback
+        resp.raise_for_status()
+
+    payload = resp.json()
+    payload["expires_at"] = time.time() + payload.get("expires_in", 0) - 60
+    _token_cache["data"] = payload
+    return payload["access_token"]
+
+def fetch_job_via_api(url: str) -> str:
+    # Extract numeric job ID
+    m = re.search(r"(?:jobs/view/|currentJobId=)(\d+)", url)
+    if not m:
         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", "")
+    job_id = m.group(1)
+
+    # Try LinkedIn Jobs API
+    try:
+        token = get_linkedin_token()
+        api_url = f"https://api.linkedin.com/v2/jobPosts/{job_id}?projection=(description)"
+        r = requests.get(api_url,
+                         headers={"Authorization": f"Bearer {token}"},
+                         timeout=10)
+        r.raise_for_status()
+        return r.json().get("description", "")
+    except Exception:
+        # Fallback to scraping
+        try:
+            page = requests.get(url, headers={"User-Agent":"Mozilla/5.0"}, timeout=10)
+            soup = BeautifulSoup(page.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 "[Error] No description found via scraping."
+        except Exception as e:
+            return f"[Scrape Error] {e}"
 
 # ──────────────────────────────────────────────────────────────────────────────
-# AI & File utilities
+# AI & File Utilities
 # ──────────────────────────────────────────────────────────────────────────────
 def ask_gemini(prompt: str, temp: float = 0.6) -> str:
     try:
@@ -109,7 +140,7 @@ def save_pdf(text: str) -> str:
     return f.name
 
 # ──────────────────────────────────────────────────────────────────────────────
-# Core application logic
+# Core Logic
 # ──────────────────────────────────────────────────────────────────────────────
 LANGS = {
     "EN": "English", "DE": "German",  "FR": "French",   "ES": "Spanish",
@@ -119,13 +150,14 @@ LANGS = {
 
 def generate_resume(name, email, phone, summary, exp, edu, skills, lang):
     prompt = f"""
-Create a professional résumé in Markdown—no first-person. Output in {LANGS[lang]}.
+Create a professional résumé in Markdown without first-person pronouns.
+Output language: {LANGS[lang]}
 
 Name: {name}
 Email: {email}
 Phone: {phone}
 
-Summary:
+Professional Summary:
 {summary}
 
 Experience:
@@ -144,9 +176,9 @@ 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):
+def score_resume(resume_md, jd):
     prompt = f"""
-Evaluate this résumé vs. the job description. Return Markdown:
+Evaluate this résumé against the job description. Return compact Markdown:
 
 ### Match Score
 <0-100>
@@ -156,27 +188,33 @@ Evaluate this résumé vs. the job description. Return Markdown:
 """
     return ask_gemini(prompt, temp=0.4)
 
-def refine_section(section, instr, lang):
-    prompt = f"Refine this résumé section in {LANGS[lang]}.\nInstruction: {instr}\nText:\n{section}"
+def refine_section(text, instr, lang):
+    prompt = f"""
+Apply the following instruction to this résumé section. Respond in {LANGS[lang]}.
+
+Instruction: {instr}
+Section:
+{text}
+"""
     out = ask_gemini(prompt)
     return translate_text(out, lang)
 
-def generate_cover_letter(resume_md, job_desc, tone, lang):
+def generate_cover_letter(resume_md, jd, tone, lang):
     prompt = f"""
-Draft a one-page cover letter (≤300 words), {tone} tone, in {LANGS[lang]}.
+Draft a one-page cover letter (max 300 words), in a {tone} tone, using {LANGS[lang]}.
 Salutation: "Dear Hiring Manager,"
 
 Résumé:
 {resume_md}
 
 Job Description:
-{job_desc}
+{jd}
 """
     letter = ask_gemini(prompt)
     return translate_text(letter, lang)
 
 # ──────────────────────────────────────────────────────────────────────────────
-# Gradio UI
+# Gradio App
 # ──────────────────────────────────────────────────────────────────────────────
 with gr.Blocks(title="AI Resume Studio") as demo:
     gr.Markdown("## 🧠 AI Resume Studio (Gemini × DeepL × LinkedIn)")
@@ -184,19 +222,23 @@ with gr.Blocks(title="AI Resume Studio") as demo:
     # 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")
-        sum_in    = gr.Textbox(label="Summary")
+            name_in, email_in, phone_in = (
+                gr.Textbox(label="Name"),
+                gr.Textbox(label="Email"),
+                gr.Textbox(label="Phone"),
+            )
+        sum_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")
 
-        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")
+        out_md   = gr.Markdown(label="Résume (Markdown)")
+        out_docx = gr.File(label="⬇ Download .docx")
+        out_pdf  = gr.File(label="⬇ Download .pdf")
+        btn_gen  = gr.Button("Generate")
 
-        gen_btn.click(
+        btn_gen.click(
             generate_and_export,
             inputs=[name_in, email_in, phone_in, sum_in, exp_in, edu_in, skills_in, lang_in],
             outputs=[out_md, out_docx, out_pdf],
@@ -204,36 +246,39 @@ with gr.Blocks(title="AI Resume Studio") as demo:
 
     # 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)
+        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)
+        btn_score = gr.Button("Evaluate")
+        btn_score.click(score_resume, inputs=[res_in, jd_in], outputs=score_out)
 
     # Tab 3: AI Section Co-Pilot
     with gr.Tab("✏️ AI Section Co-Pilot"):
         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")
+        act_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, action, sec_lang], outputs=sec_out)
+        btn_sec  = gr.Button("Apply")
+        btn_sec.click(refine_section, inputs=[sec_in, act_in, lang_sec], outputs=sec_out)
 
     # Tab 4: Cover-Letter Generator
     with gr.Tab("📧 Cover-Letter Generator"):
-        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)
+        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")
+        btn_cv   = gr.Button("Generate")
+        btn_cv.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])
+        btn_fetch = gr.Button("Fetch from LinkedIn")
+        btn_fetch.click(fetch_job_via_api, inputs=[url_in], outputs=[jd_out])
 
 demo.launch(share=False)