Update app.py

app.py

@@ -44,7 +44,7 @@ def fetch_arbeitnow(keywords):
     return []
 
 def fetch_remotive(keywords):
-    resp = requests.get("https://remotive.io/api/remote-jobs", params={"search": " ".join(keywords)})
+    resp = requests.get("https://remotive.com/api/remote-jobs", params={"search": " ".join(keywords)})
     print("Remotive", resp)
     if resp.ok:
         return resp.json().get("jobs", [])
@@ -86,24 +86,44 @@ def find_jobs(file, added_kw, use_ai):
     keywords = [kw.strip() for kw in added_kw.split(",") if kw.strip()] or extracted
     print("Keywords", keywords)
     jobs = fetch_arbeitnow(keywords) + fetch_remotive(keywords) + fetch_remoteok(keywords)
+    print("Jobs", jobs)
     ranked = rank_jobs(resume, jobs)
+    print("Rank_jobs", rank_jobs)
 
     table = []
-    for j, score in ranked:
-        raw_date = j.get("publication_date") or j.get("created_at") or j.get("date")
-        posted = str(raw_date)[:10] if raw_date is not None else ""
+    for job, score in ranked:
+        role     = job.get("title") or job.get("position", "")
+        company  = job.get("company") or job.get("company_name", "")
+        location = job.get("location", "")
+        # Normalize date (as we did before)
+        posted   = format_posted(job)
+        apply_url= job.get("url") or job.get("apply_url","") or ""
+        # Make sure none of these are dicts/lists
         table.append({
-            "Role":     j.get("title","N/A") or j.get("position","N/A"),
-            "Company":  j.get("company","N/A") or j.get("company_name","N/A"),
-            "Location": j.get("location","N/A"),
-            "Posted":   posted,
+            "Role":     str(role),
+            "Company":  str(company),
+            "Location": str(location),
+            "Posted":   str(posted),
             "Score":    f"{score*100:.1f}%",
-            "Apply":    j.get("url") or j.get("joblink") or j.get("apply_url") or ""
+            "Apply":    str(apply_url)
         })
 
     explanation = refine_with_ai(ranked, resume) if use_ai else ""
     return table, explanation
 
+# 7️⃣ Jobs in Markdown
+def jobs_to_markdown(table):
+    md  = "| Role | Company | Location | Posted | Score | Apply |\n"
+    md += "| ---  | ------- | -------- | ------ | ----- | ----- |\n"
+    for row in table:
+        link = f"[Apply]({row['Apply']})" if row['Apply'] else ""
+        md += (
+            f"| {row['Role']} | {row['Company']} "
+            f"| {row['Location']} | {row['Posted']} "
+            f"| {row['Score']} | {link} |\n"
+        )
+    return md
+
 
 # 7️⃣ Gradio UI
 with gr.Blocks() as demo:
@@ -113,9 +133,12 @@ with gr.Blocks() as demo:
         added = gr.Textbox(label="Add keywords (comma-separated)", placeholder="e.g. Python, ML")
     use_ai = gr.Checkbox(label="Use AI to refine explanation", value=False)
     btn = gr.Button("Find Jobs")
-    result = gr.Dataframe(headers=["Role","Company","Location","Posted","Score","Apply"], row_count=(1,5))
-    ai_out = gr.Markdown()
-    btn.click(find_jobs, [resume, added, use_ai], [result, ai_out])
+    jobs_md = gr.Markdown()
+    btn.click(
+        fn=lambda f,k,ai: jobs_to_markdown(*find_jobs(f,k,ai)),
+        inputs=[resume, added, use_ai],
+        outputs=[jobs_md]
+    )
 
 if __name__ == "__main__":
     demo.launch()