Update app.py

app.py

@@ -13,6 +13,7 @@ Features
    • LinkedIn via OAuth2 Jobs API
    • All other sites via HTML scraping
 6. Multilingual export via Deep-Translator (DeepL backend)
+7. Auto-populate Score tab from latest Resume & JD
 """
 
 import os
@@ -79,61 +80,47 @@ def get_linkedin_token():
 # ──────────────────────────────────────────────────────────────────────────────
 def fetch_job_description(url: str) -> str:
     domain = urlparse(url).netloc.lower()
-    # 1) If it's LinkedIn, try the Jobs API first
     if "linkedin.com" in domain:
         m = re.search(r"(?:jobs/view/|currentJobId=)(\d+)", url)
         if m:
             job_id = m.group(1)
             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
-                )
+                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:
-                # fall through to generic scraping
                 pass
 
-    # 2) Generic scraping for any other site (or LinkedIn fallback)
     try:
         page = requests.get(url, headers={"User-Agent":"Mozilla/5.0"}, timeout=10)
         soup = BeautifulSoup(page.text, "html.parser")
-
-        # Common job-description selectors across platforms
         selectors = [
-            "div.jobsearch-jobDescriptionText",      # Indeed
-            "section.description",                   # generic
-            "div.jobs-description__content",         # LinkedIn client-side
-            "div#job-details",                       # Greenhouse
-            "article.jobPosting",                    # Workday / custom
-            "div.jd-container",                      # many ATS
+            "div.jobsearch-jobDescriptionText",
+            "section.description",
+            "div.jobs-description__content",
+            "div#job-details",
+            "article.jobPosting",
+            "div.jd-container",
         ]
         for sel in selectors:
             block = soup.select_one(sel)
             if block and block.get_text(strip=True):
                 return block.get_text(" ", strip=True)
-
-        # Fallback: return full page text (truncated)
         text = soup.get_text(" ", strip=True)
         return text[:5000] + ("…" if len(text) > 5000 else "")
     except Exception as e:
         return f"[Error fetching job description] {e}"
 
 # ──────────────────────────────────────────────────────────────────────────────
-# AI & File Utilities (unchanged)
+# AI & File Utilities
 # ──────────────────────────────────────────────────────────────────────────────
 def ask_gemini(prompt: str, temp: float = 0.6) -> str:
     try:
-        return GEMINI.generate_content(
-            prompt, generation_config={"temperature": temp}
-        ).text.strip()
+        return GEMINI.generate_content(prompt, generation_config={"temperature": temp}).text.strip()
     except Exception as e:
         return f"[Gemini Error] {e}"
 
@@ -160,7 +147,7 @@ def save_pdf(text: str) -> str:
     return f.name
 
 # ──────────────────────────────────────────────────────────────────────────────
-# Core AI Logic & UI Setup (unchanged)
+# Core AI Logic
 # ──────────────────────────────────────────────────────────────────────────────
 LANGS = {
     "EN": "English", "DE": "German",  "FR": "French",   "ES": "Spanish",
@@ -234,18 +221,23 @@ Job Description:
     return translate_text(letter, lang)
 
 # ──────────────────────────────────────────────────────────────────────────────
-# Gradio App Definition
+# Gradio App Definition with State for Auto-Populate
 # ──────────────��───────────────────────────────────────────────────────────────
 with gr.Blocks(title="AI Resume Studio") as demo:
     gr.Markdown("## 🧠 AI Resume Studio (Gemini × DeepL + Universal Job Fetcher)")
 
+    # State to hold last-generated résumé & JD
+    resume_state = gr.State(value="")
+    jd_state     = gr.State(value="")
+
     # Tab 1: Generate Résumé
     with gr.Tab("📄 Generate Résumé"):
-        name_in, email_in, phone_in = (
-            gr.Textbox(label="Name"),
-            gr.Textbox(label="Email"),
-            gr.Textbox(label="Phone")
-        )
+        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="Professional Summary")
         exp_in    = gr.Textbox(label="Experience")
         edu_in    = gr.Textbox(label="Education")
@@ -260,13 +252,13 @@ with gr.Blocks(title="AI Resume Studio") as demo:
         btn_gen.click(
             generate_and_export,
             inputs=[name_in, email_in, phone_in, sum_in, exp_in, edu_in, skills_in, lang_in],
-            outputs=[md_out, docx_out, pdf_out],
+            outputs=[md_out, docx_out, pdf_out, resume_state],
         )
 
     # 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(value=resume_state, label="Résumé (Markdown)", lines=10)
+        jd_in     = gr.Textbox(value=jd_state,     label="Job Description",   lines=8)
         score_out = gr.Markdown(label="Score & Suggestions")
         btn_score = gr.Button("Evaluate")
         btn_score.click(score_resume, inputs=[res_in, jd_in], outputs=score_out)
@@ -274,9 +266,10 @@ with gr.Blocks(title="AI Resume Studio") as demo:
     # Tab 3: AI Section Co-Pilot
     with gr.Tab("✏️ AI Section Co-Pilot"):
         sec_in   = gr.Textbox(label="Section Text", lines=6)
-        act_in   = gr.Radio([
-            "Rewrite", "Make More Concise", "Quantify Achievements", "Convert to Bullet Points"
-        ], label="Action")
+        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)
         btn_sec  = gr.Button("Apply")
@@ -286,7 +279,7 @@ with gr.Blocks(title="AI Resume Studio") as demo:
     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_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")
@@ -299,6 +292,10 @@ with gr.Blocks(title="AI Resume Studio") as demo:
         url_in    = gr.Textbox(label="Job URL")
         jd_out    = gr.Textbox(label="Job Description", lines=12)
         btn_fetch = gr.Button("Fetch Description")
-        btn_fetch.click(fetch_job_description, inputs=[url_in], outputs=[jd_out])
+        btn_fetch.click(
+            fetch_job_description,
+            inputs=[url_in],
+            outputs=[jd_out, jd_state],
+        )
 
 demo.launch(share=False)