Update app.py

app.py

@@ -293,19 +293,18 @@ def fetch_job_listings(job_title):
         if job_data.get('jobs'):
             job_listings = []
             for job in job_data['jobs'][:5]:  # Limit to 5 job listings
-                job_info = {
-                    "Job Title": job.get('title', 'N/A'),
-                    "Company": job.get('company', 'N/A'),
-                    "Location": job.get('location', 'N/A'),
-                    "Salary": job.get('salary', 'Not specified')
-                }
-                job_listings.append(job_info)
+                job_listings.append([
+                    job.get('title', 'N/A'),
+                    job.get('company', 'N/A'),
+                    job.get('location', 'N/A'),
+                    job.get('salary', 'Not specified')
+                ])
             return job_listings
         else:
-            return [{"Message": "No job listings found for this career path."}]
+            return [['No job listings', 'found', 'for this', 'career path']]
     
     except requests.RequestException as e:
-        return [{"Error": f"Could not fetch job listings: {str(e)}"}]
+        return [['Error', 'fetching', 'job listings', str(e)]]
 
 # Prediction function (modified to return job suggestions)
 def rfprediction(model_choice, name, logical_thinking, hackathon_attend, coding_skills, public_speaking_skills,
@@ -423,7 +422,10 @@ worker_list = ["hard worker", "smart worker"]
 def create_output_component():
     return [
         gr.Label(label="Career Probabilities"),
-        gr.Dataframe(label="Job Suggestions")
+        gr.Dataframe(
+            headers=["Job Title", "Company", "Location", "Salary"],
+            label="Job Suggestions"
+        )
     ]
 
 demo = gr.Interface(
@@ -458,5 +460,4 @@ demo = gr.Interface(
 
 # Main execution
 if __name__ == "__main__":
-    demo.launch(share=True)
-
+    demo.launch(share=True)