Update app.py

app.py

@@ -1,43 +1,51 @@
 """
-AI Resume Studio – Hugging Face Space
-Author: Oluwafemi Idiakhoa
-Last update: 2025‑06‑27
+AI Resume Studio – Hugging Face Space
+Author: Oluwafemi Idiakhoa
+Last update: 2025-06-27
 
 Features
 ────────
 1. Generate resume → Word & PDF downloads
 2. Score resume against job description
-3. AI Section Co‑Pilot (rewrite, quantify, bulletize…)
-4. Cover‑letter generator
-5. Job‑description scraper by URL
-6. Multilingual export via DeepL (free & pro keys supported)
+3. AI Section Co-Pilot (rewrite, quantify, bulletize…)
+4. Cover-letter generator
+5. Job-description scraper by URL
+6. Multilingual export via Deep-Translator (DeepL backend)
 """
 
-# ──────────────────────────────────────────────────────────────────────────────
-# Imports & setup
-# ──────────────────────────────────────────────────────────────────────────────
-import os, tempfile
+import os
+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                 # python‑docx
+from docx import Document            # python-docx
 from reportlab.lib.pagesizes import LETTER
 from reportlab.pdfgen import canvas
-import deepl                              # DeepL translation
+from deep_translator import DeeplTranslator
 
+# ──────────────────────────────────────────────────────────────────────────────
+# Environment & Model Setup
+# ──────────────────────────────────────────────────────────────────────────────
 load_dotenv()
 
-# Gemini model (make sure key has access to this version)
+# Gemini (DeepMind) setup
 genai.configure(api_key=os.getenv("API_KEY"))
-GEMINI_MODEL = genai.GenerativeModel("gemini-1.5-pro-latest")
+GEMINI = genai.GenerativeModel("gemini-1.5-pro-latest")
 
-# DeepL translator
+# DeepL via Deep-Translator
 DEEPL_KEY = os.getenv("DEEPL_API_KEY")
-DEEPL_TRANSLATOR = deepl.Translator(DEEPL_KEY) if DEEPL_KEY else None
+# 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
+    try:
+        return DeeplTranslator(api_key=DEEPL_KEY, target=target_code).translate(text)
+    except Exception as e:
+        return f"[Translation Error] {e}\n\n{text}"
 
-# Supported DeepL target languages (code → label)
+# Supported languages mapping
 LANGS = {
     "EN": "English",
     "DE": "German",
@@ -45,231 +53,189 @@ LANGS = {
     "ES": "Spanish",
     "IT": "Italian",
     "NL": "Dutch",
-    "PT-PT": "Portuguese",
-    "PT-BR": "Portuguese (BR)",
+    "PT": "Portuguese",
     "PL": "Polish",
     "JA": "Japanese",
     "ZH": "Chinese",
 }
 
 # ──────────────────────────────────────────────────────────────────────────────
-# Helpers
+# AI / File Helpers
 # ──────────────────────────────────────────────────────────────────────────────
-def ask_gemini(prompt: str, temperature: float = 0.6) -> str:
+def ask_gemini(prompt: str, temp: float = 0.6) -> str:
     try:
-        rsp = GEMINI_MODEL.generate_content(
-            prompt, generation_config={"temperature": temperature}
-        )
-        return rsp.text.strip()
-    except Exception as err:
-        return f"[Gemini Error] {err}"
-
-def translate_if_needed(text: str, target_code: str) -> str:
-    if target_code == "EN" or not DEEPL_TRANSLATOR:
-        return text  # already English or DeepL key missing
-    try:
-        res = DEEPL_TRANSLATOR.translate_text(text, target_lang=target_code)
-        return res.text
-    except Exception as err:
-        return f"[DeepL Error] {err}\n\n{text}"
+        res = GEMINI.generate_content(prompt, generation_config={"temperature": temp})
+        return res.text.strip()
+    except Exception as e:
+        return f"[Gemini Error] {e}"
 
-def save_docx(text: str) -> str:
-    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".docx")
+def save_docx(content: str) -> str:
+    f = tempfile.NamedTemporaryFile(delete=False, suffix=".docx")
     doc = Document()
-    for line in text.splitlines():
+    for line in content.splitlines():
         doc.add_paragraph(line)
-    doc.save(tmp.name)
-    return tmp.name
+    doc.save(f.name)
+    return f.name
 
-def save_pdf(text: str) -> str:
-    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
-    c = canvas.Canvas(tmp.name, pagesize=LETTER)
+def save_pdf(content: 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 text.splitlines():
+    for line in content.splitlines():
         c.drawString(72, y, line)
         y -= 14
         if y < 72:
             c.showPage()
             y = height - 72
     c.save()
-    return tmp.name
+    return f.name
 
 # ──────────────────────────────────────────────────────────────────────────────
-# Core AI functions
+# Core Features
 # ──────────────────────────────────────────────────────────────────────────────
-def generate_resume(name, email, phone, summary, experience, education, skills, lang):
+def generate_resume(name, email, phone, summary, exp, edu, skills, lang_code):
     prompt = f"""
-Create a professional resume in Markdown. **Do not use first‑person pronouns.**
-
-Target language: {LANGS[lang]}
+Create a professional résumé in Markdown without first-person pronouns.
+Output language: {LANGS[lang_code]}
 
 Name: {name}
 Email: {email}
 Phone: {phone}
 
-Professional Summary:
+Professional Summary:
 {summary}
 
 Experience:
-{experience}
+{exp}
 
 Education:
-{education}
+{edu}
 
 Skills:
 {skills}
 """
-    resume_md = ask_gemini(prompt)
-    return translate_if_needed(resume_md, lang)
+    md = ask_gemini(prompt)
+    return translate_text(md, lang_code)
 
 def score_resume(resume_md, job_desc):
     prompt = f"""
-Evaluate the RESUME against the JOB DESCRIPTION. Return *only* compact Markdown:
-### Match Score
-<integer 0‑100>
+Evaluate this résumé against the job description. Return compact Markdown:
+
+### Match Score
+<0-100>
 
 ### Suggestions
-- <bullet 1>
-- <bullet 2>
-- <bullet 3>
+- suggestion 1
+- suggestion 2
 """
-    return ask_gemini(prompt.format(resume_md=resume_md, job_desc=job_desc), temperature=0.4)
+    return ask_gemini(prompt, temp=0.4)
 
-def refine_section(section_text, instruction, lang):
+def refine_section(section, instruction, lang_code):
     prompt = f"""
-Perform the following instruction on the resume section. Respond in {LANGS[lang]}.
+Apply the following instruction to this résumé section. Respond in {LANGS[lang_code]}.
 
 Instruction: {instruction}
-Text:
-{section_text}
+Section:
+{section}
 """
-    refined = ask_gemini(prompt)
-    return translate_if_needed(refined, lang)
+    out = ask_gemini(prompt)
+    return translate_text(out, lang_code)
 
-def generate_cover_letter(resume_md, job_desc, tone, lang):
+def generate_cover_letter(resume_md, job_desc, tone, lang_code):
     prompt = f"""
-Draft a one‑page cover letter (≤ 300 words) in {tone} tone, aligning the RESUME
-to the JOB DESCRIPTION. Use {LANGS[lang]} throughout. Salutation: "Dear Hiring Manager,".
+Draft a one-page cover letter (max 300 words) in a {tone} tone, matching this résumé to the job description. Use {LANGS[lang_code]}.
+Salutation: "Dear Hiring Manager,"
 
-RESUME:
+Résumé:
 {resume_md}
 
-JOB DESCRIPTION:
+Job Description:
 {job_desc}
 """
     letter = ask_gemini(prompt)
-    return translate_if_needed(letter, lang)
+    return translate_text(letter, lang_code)
 
-# ──────────────────────────────────────────────────────────────────────────────
-# JD scraper
-# ──────────────────────────────────────────────────────────────────────────────
 def scrape_job_description(url):
     try:
-        headers = {"User-Agent": "Mozilla/5.0"}
-        page = requests.get(url, headers=headers, timeout=10)
-        soup = BeautifulSoup(page.text, "html.parser")
-
-        # Heuristics for popular boards
-        selectors = [
-            "div.jobsearch-jobDescriptionText",          # Indeed
-            "section.description",                       # generic
-            "div.jobs-description__content",             # LinkedIn
-            "div#job-details"                            # Greenhouse
-        ]
-        for sel in selectors:
+        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"[Error] {e}"
+        return f"[Scrape Error] {e}"
 
-# ──────────────────────────────────────────────────────────────────────────────
-# Wrapper to export resume + files
-# ──────────────────────────────────────────────────────────────────────────────
-def generate_resume_and_files(name, email, phone, summary,
-                              experience, education, skills, lang):
-    resume_md = generate_resume(name, email, phone, summary,
-                                experience, education, skills, lang)
-    return resume_md, save_docx(resume_md), save_pdf(resume_md)
+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)
 
 # ──────────────────────────────────────────────────────────────────────────────
 # Gradio UI
 # ──────────────────────────────────────────────────────────────────────────────
 with gr.Blocks(title="AI Resume Studio") as demo:
-    gr.Markdown("## 🧠 Interactive AI Resume Studio (Gemini × DeepL)")
-
-    LANG_CHOICES = [(v, k) for k, v in LANGS.items()]
+    gr.Markdown("## 🧠 Interactive AI Resume Studio (Gemini × DeepL)")
 
-    # ── 1 · Resume generation ────────────────────────────────────────────────
+    # Generate Resume
     with gr.Tab("📄 Generate Resume"):
         with gr.Row():
-            name  = gr.Textbox(label="Name")
-            email = gr.Textbox(label="Email")
-            phone = gr.Textbox(label="Phone")
-        summary    = gr.Textbox(label="Professional Summary")
-        experience = gr.Textbox(label="Experience")
-        education  = gr.Textbox(label="Education")
-        skills     = gr.Textbox(label="Skills")
-        lang_sel   = gr.Dropdown(LANG_CHOICES, value="EN", label="Output Language")
-
-        resume_md  = gr.Markdown(label="Generated Resume")
-        docx_file  = gr.File(label="⬇ Word (.docx)")
-        pdf_file   = gr.File(label="⬇ PDF (.pdf)")
-        gen_btn    = gr.Button("Generate Resume")
+            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")
+
+        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")
 
         gen_btn.click(
-            generate_resume_and_files,
-            inputs=[name, email, phone, summary, experience, education, skills, lang_sel],
-            outputs=[resume_md, docx_file, pdf_file],
+            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],
         )
 
-    # ── 2 · Match score ──────────────────────────────────────────────────────
+    # Score Resume
     with gr.Tab("🧮 Score Resume Against Job"):
-        resume_in = gr.Textbox(label="Resume (Markdown)", lines=12)
-        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=[resume_in, jd_in], outputs=score_out)
-
-    # ── 3 · AI Section Co‑Pilot ──────────────────────────────────────────────
-    with gr.Tab("✏️ AI Section Co‑Pilot"):
-        sec_text = 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(LANG_CHOICES, 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_text, action, sec_lang], outputs=sec_out
-        )
-
-    # ── 4 · Cover‑letter generator ───────────────────────────────────────────
-    with gr.Tab("📧 Cover‑Letter Generator"):
-        cv_resume = gr.Textbox(label="Resume (Markdown)", lines=12)
-        cv_jd     = gr.Textbox(label="Job Description",  lines=8)
+        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)
+
+    # 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
+    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(LANG_CHOICES, value="EN", label="Language")
-        cv_out    = gr.Markdown(label="Cover Letter")
-        cv_btn    = gr.Button("Generate Cover Letter")
-
-        cv_btn.click(
-            generate_cover_letter,
-            inputs=[cv_resume, cv_jd, cv_tone, cv_lang],
-            outputs=cv_out,
-        )
+        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)
 
-    # ── 5 · Job‑description scraper ──────────────────────────────────────────
+    # 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 Description")
+        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)
+        scrape_btn.click(scrape_job_description, inputs=[url_in], outputs=[jd_out])
 
-demo.launch(share=False)  # HF Spaces already publishes the app
+demo.launch(share=False)