Update app.py

app.py

@@ -6,7 +6,6 @@ import sklearn
 from datasets import load_dataset
 import joblib
 import requests
-import os
 
 # Read the data
 data = pd.read_csv("mldata.csv")
@@ -50,45 +49,121 @@ career_interest_references = create_embedding_dict('interested career area ')
 company_intends_references = create_embedding_dict('Type of company want to settle in?')
 book_interest_references = create_embedding_dict('Interested Type of Books')
 
-# Function to fetch job listings
+# Function to fetch job listings using JSearch API
 def fetch_job_listings(job_title):
-    # Use environment variable for API key (more secure)
-    api_key = os.environ.get('RAPIDAPI_KEY', '714f5a2539msh798d996c3243876p19c71ajsnfcd7ce481cb9')
+    """Fetch job listings using JSearch API from RapidAPI"""
+    api_key = '714f5a2539msh798d996c3243876p19c71ajsnfcd7ce481cb9'
+    
+    url = "https://jsearch.p.rapidapi.com/search"
     
-    url = "https://jobs-api14.p.rapidapi.com/v2/list"
     querystring = {
-        "query": job_title,
-        "location": "India",
-        "autoTranslateLocation": "false",
-        "remoteOnly": "false",
-        "employmentTypes": "fulltime;parttime;intern;contractor"
+        "query": f"{job_title} in India",
+        "page": "1",
+        "num_pages": "1",
+        "date_posted": "all"
     }
+    
     headers = {
         "x-rapidapi-key": api_key,
-        "x-rapidapi-host": "jobs-api14.p.rapidapi.com"
+        "x-rapidapi-host": "jsearch.p.rapidapi.com"
     }
 
     try:
-        response = requests.get(url, headers=headers, params=querystring, timeout=10)
-        response.raise_for_status()
-        job_data = response.json()
+        response = requests.get(url, headers=headers, params=querystring, timeout=15)
+        
+        print(f"JSearch API Response Status: {response.status_code}")
         
-        # Process and format job listings
-        if job_data.get('jobs'):
-            job_listings = []
-            for job in job_data['jobs'][:5]:  # Limit to 5 job listings
-                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
+        if response.status_code == 200:
+            job_data = response.json()
+            
+            # Process JSearch response format
+            if job_data.get('data') and len(job_data['data']) > 0:
+                job_listings = []
+                for job in job_data['data'][:5]:  # Limit to 5 job listings
+                    # Extract salary information
+                    salary = "Not specified"
+                    if job.get('job_min_salary') and job.get('job_max_salary'):
+                        min_sal = job.get('job_min_salary')
+                        max_sal = job.get('job_max_salary')
+                        currency = job.get('job_salary_currency', 'INR')
+                        if currency == 'INR':
+                            salary = f"₹{min_sal:,.0f} - ₹{max_sal:,.0f}"
+                        else:
+                            salary = f"{currency} {min_sal:,.0f} - {max_sal:,.0f}"
+                    elif job.get('job_min_salary'):
+                        min_sal = job.get('job_min_salary')
+                        currency = job.get('job_salary_currency', 'INR')
+                        if currency == 'INR':
+                            salary = f"₹{min_sal:,.0f}+"
+                        else:
+                            salary = f"{currency} {min_sal:,.0f}+"
+                    
+                    # Extract location
+                    location_parts = []
+                    if job.get('job_city'):
+                        location_parts.append(job.get('job_city'))
+                    if job.get('job_state'):
+                        location_parts.append(job.get('job_state'))
+                    if not location_parts and job.get('job_country'):
+                        location_parts.append(job.get('job_country'))
+                    location = ', '.join(location_parts) if location_parts else 'India'
+                    
+                    job_listings.append([
+                        job.get('job_title', 'N/A'),
+                        job.get('employer_name', 'N/A'),
+                        location,
+                        salary
+                    ])
+                
+                print(f"Successfully fetched {len(job_listings)} jobs")
+                return job_listings
+            else:
+                print("No jobs found in API response")
+                return generate_placeholder_jobs(job_title)
         else:
-            return [['No job listings', 'found', 'for this', 'career path']]
+            print(f"JSearch API Error: {response.status_code} - {response.text[:200]}")
+            return generate_placeholder_jobs(job_title)
     
-    except requests.RequestException as e:
-        return [['Error', 'fetching', 'job listings', str(e)]]
+    except Exception as e:
+        print(f"JSearch Exception: {str(e)}")
+        return generate_placeholder_jobs(job_title)
+
+# Function to generate placeholder job suggestions
+def generate_placeholder_jobs(career_title):
+    """Generate helpful placeholder job suggestions when API fails"""
+    job_resources = [
+        [
+            f"{career_title} (Entry Level)",
+            "Various Companies",
+            "India (Remote/Onsite)",
+            "Check: LinkedIn, Naukri.com, Indeed"
+        ],
+        [
+            f"{career_title} (Mid Level)",
+            "Various Companies",
+            "India (Remote/Onsite)",
+            "Check: LinkedIn, Naukri.com, Indeed"
+        ],
+        [
+            f"{career_title} Intern",
+            "Various Companies",
+            "India (Remote/Onsite)",
+            "Check: Internshala, AngelList"
+        ],
+        [
+            "Job Search Tips",
+            "💡 Recommended Platforms:",
+            "LinkedIn • Naukri • Indeed",
+            "Glassdoor • AngelList • Instahyre"
+        ],
+        [
+            "Next Steps",
+            "Build portfolio projects",
+            "Network on LinkedIn",
+            "Apply to 10+ positions daily"
+        ]
+    ]
+    return job_resources
 
 # Prediction function (modified to return job suggestions)
 def rfprediction(model_choice, name, logical_thinking, hackathon_attend, coding_skills, public_speaking_skills,
@@ -250,4 +325,4 @@ demo = gr.Interface(
 
 # Main execution
 if __name__ == "__main__":
-    demo.launch(share=False)  # share=True not supported on HF Spaces
+    demo.launch(share=False)