Update app.py

app.py

@@ -1,85 +1,79 @@
-import os, tempfile, datetime
+"""
+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)
+"""
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Imports & setup
+# ──────────────────────────────────────────────────────────────────────────────
+import os, tempfile
+import requests
 import gradio as gr
 import google.generativeai as genai
 from dotenv import load_dotenv
-
-# ── 3rd‑party formatters ──────────────────────────────────────────────────────
-from docx import Document                     # python-docx
-from reportlab.lib.pagesizes import LETTER    # reportlab
+from bs4 import BeautifulSoup
+from docx import Document                 # python‑docx
+from reportlab.lib.pagesizes import LETTER
 from reportlab.pdfgen import canvas
+import deepl                              # DeepL translation
 
-# ──  Environment & Model  ──────────────────────────────────────────────────────
 load_dotenv()
-API_KEY = os.getenv("GEMINI_API_KEY")
-genai.configure(api_key=API_KEY)
-model = genai.GenerativeModel("gemini-1.5-pro-latest")
-
-# ──  Core AI helpers  ─────────────────────────────────────────────────────────
-def _ask_gemini(prompt: str) -> str:
-    """Utility wrapper with basic error‐handling."""
-    try:
-        rsp = model.generate_content(prompt, generation_config={"temperature": 0.7})
-        return rsp.text.strip()
-    except Exception as exc:
-        return f"[Gemini Error] {exc}"
-
-# ──  Business Logic  ──────────────────────────────────────────────────────────
-def generate_resume(name, email, phone, summary, experience, education, skills):
-    prompt = f"""
-    Create a professionally formatted resume. Use Markdown headings and no first‑person pronouns.
-
-    Name: {name}
-    Email: {email}
-    Phone: {phone}
-
-    Professional Summary:
-    {summary}
-
-    Experience:
-    {experience}
 
-    Education:
-    {education}
+# Gemini model (make sure key has access to this version)
+genai.configure(api_key=os.getenv("API_KEY"))
+GEMINI_MODEL = genai.GenerativeModel("gemini-1.5-pro-latest")
 
-    Skills:
-    {skills}
-    """
-    return _ask_gemini(prompt)
+# DeepL translator
+DEEPL_KEY = os.getenv("DEEPL_API_KEY")
+DEEPL_TRANSLATOR = deepl.Translator(DEEPL_KEY) if DEEPL_KEY else None
 
-def score_resume(resume, job_desc):
-    prompt = f"""
-    Evaluate the RESUME against the JOB DESCRIPTION. Return JSON:
-    {{
-      "score": <0‑100>,
-      "suggestions": [ "…", "…" ]
-    }}
-    RESUME:
-    {resume}
-
-    JOB DESCRIPTION:
-    {job_desc}
-    """
-    return _ask_gemini(prompt)
-
-def refine_text(section_text, instruction):
-    prompt = f"Refine the following text.\nInstruction: {instruction}\nText:\n{section_text}"
-    return _ask_gemini(prompt)
-
-def generate_cover_letter(resume, job_desc, tone):
-    prompt = f"""
-    Act as a career coach. Draft a one‑page cover letter **in {tone} tone** that aligns this RESUME
-    to the JOB DESCRIPTION. Do not exceed 300 words.
+# Supported DeepL target languages (code → label)
+LANGS = {
+    "EN": "English",
+    "DE": "German",
+    "FR": "French",
+    "ES": "Spanish",
+    "IT": "Italian",
+    "NL": "Dutch",
+    "PT-PT": "Portuguese",
+    "PT-BR": "Portuguese (BR)",
+    "PL": "Polish",
+    "JA": "Japanese",
+    "ZH": "Chinese",
+}
 
-    RESUME:
-    {resume}
+# ──────────────────────────────────────────────────────────────────────────────
+# Helpers
+# ──────────────────────────────────────────────────────────────────────────────
+def ask_gemini(prompt: str, temperature: 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}"
 
-    JOB DESCRIPTION:
-    {job_desc}
-    """
-    return _ask_gemini(prompt)
+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}"
 
-# ──  File‑export helpers  ──────────────────────────────────────────────────────
-def _to_docx(text: str) -> str:
+def save_docx(text: str) -> str:
     tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".docx")
     doc = Document()
     for line in text.splitlines():
@@ -87,7 +81,7 @@ def _to_docx(text: str) -> str:
     doc.save(tmp.name)
     return tmp.name
 
-def _to_pdf(text: str) -> str:
+def save_pdf(text: str) -> str:
     tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
     c = canvas.Canvas(tmp.name, pagesize=LETTER)
     width, height = LETTER
@@ -101,80 +95,181 @@ def _to_pdf(text: str) -> str:
     c.save()
     return tmp.name
 
-def generate_resume_and_files(*inputs):
-    resume_md = generate_resume(*inputs)
-    return (
-        resume_md,
-        _to_docx(resume_md),
-        _to_pdf(resume_md),
-    )
-
-# ──  Gradio UI  ───────────────────────────────────────────────────────────────
-with gr.Blocks(title="AI Resume Builder using Gemini") as demo:
-    gr.Markdown("## 🧠 AI Resume Builder × Gemini")
-
-    # ── Tab 1: Resume generation ─────────────────────────────────────────────
-    with gr.Tab("📄 Generate Resume"):
-        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")
-
-        resume_out = gr.Markdown(label="Generated Resume")
+# ──────────────────────────────────────────────────────────────────────────────
+# Core AI functions
+# ──────────────────────────────────────────────────────────────────────────────
+def generate_resume(name, email, phone, summary, experience, education, skills, lang):
+    prompt = f"""
+Create a professional resume in Markdown. **Do not use first‑person pronouns.**
+
+Target language: {LANGS[lang]}
+
+Name: {name}
+Email: {email}
+Phone: {phone}
+
+Professional Summary:
+{summary}
+
+Experience:
+{experience}
+
+Education:
+{education}
+
+Skills:
+{skills}
+"""
+    resume_md = ask_gemini(prompt)
+    return translate_if_needed(resume_md, lang)
+
+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>
+
+### Suggestions
+- <bullet 1>
+- <bullet 2>
+- <bullet 3>
+"""
+    return ask_gemini(prompt.format(resume_md=resume_md, job_desc=job_desc), temperature=0.4)
+
+def refine_section(section_text, instruction, lang):
+    prompt = f"""
+Perform the following instruction on the resume section. Respond in {LANGS[lang]}.
+
+Instruction: {instruction}
+Text:
+{section_text}
+"""
+    refined = ask_gemini(prompt)
+    return translate_if_needed(refined, lang)
+
+def generate_cover_letter(resume_md, job_desc, tone, lang):
+    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,".
+
+RESUME:
+{resume_md}
+
+JOB DESCRIPTION:
+{job_desc}
+"""
+    letter = ask_gemini(prompt)
+    return translate_if_needed(letter, lang)
+
+# ──────────────────────────────────────────────────────────────────────────────
+# 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:
+            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}"
+
+# ──────────────────────────────────────────────────────────────────────────────
+# 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)
+
+# ──────────────────────────────────────────────────────────────────────────────
+# 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()]
+
+    # ── 1 · Resume generation ────────────────────────────────────────────────
+    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")
 
         gen_btn.click(
             generate_resume_and_files,
-            inputs=[name, email, phone, summary, experience, education, skills],
-            outputs=[resume_out, docx_file, pdf_file],
+            inputs=[name, email, phone, summary, experience, education, skills, lang_sel],
+            outputs=[resume_md, docx_file, pdf_file],
         )
 
-    # ── Tab 2: Job‑match score ───────────────────────────────────────────────
-    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")
+    # ── 2 · Match score ──────────────────────────────────────────────────────
+    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")
 
-        def parse_score(rsp):
-            return rsp    # we leave JSON parsing to the user for now
+        score_btn.click(score_resume, inputs=[resume_in, jd_in], outputs=score_out)
 
-        score_btn.click(
-            lambda r, jd: parse_score(score_resume(r, jd)),
-            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")
 
-    # ── Tab 3: Refine section ────────────────────────────────────────────────
-    with gr.Tab("✏️ Refine Resume Section"):
-        sec_in   = gr.Textbox(label="Section Text", lines=6)
-        instr_in = gr.Textbox(label="Instruction (e.g., make concise)")
-        refined  = gr.Textbox(label="Refined Output", lines=6)
-        refine_btn = gr.Button("Refine")
-
-        refine_btn.click(
-            lambda text, instr: refine_text(text, instr),
-            inputs=[sec_in, instr_in],
-            outputs=refined,
+        sec_btn.click(
+            refine_section, inputs=[sec_text, action, sec_lang], outputs=sec_out
         )
 
-    # ── Tab 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)
-        cv_tone     = gr.Radio(["Professional", "Friendly", "Enthusiastic"], value="Professional", label="Tone")
-        cv_out      = gr.Markdown(label="Generated Cover Letter")
-        cv_btn      = gr.Button("Generate Cover Letter")
+    # ── 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)
+        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(
-            lambda r, jd, tone: generate_cover_letter(r, jd, tone),
-            inputs=[cv_resume, cv_jd, cv_tone],
+            generate_cover_letter,
+            inputs=[cv_resume, cv_jd, cv_tone, cv_lang],
             outputs=cv_out,
         )
 
-demo.launch(share=False)   # ← Spaces already exposes the public URL
+    # ── 5 · 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")
+
+        scrape_btn.click(scrape_job_description, inputs=url_in, outputs=jd_out)
+
+demo.launch(share=False)  # HF Spaces already publishes the app