Update app.py

app.py

@@ -1,7 +1,3 @@
-
-
-
-
 import gradio as gr
 import pandas as pd 
 import numpy as np 
@@ -17,7 +13,7 @@ data = pd.read_csv("mldata.csv")
 # Function to load model based on selection
 def load_model(model_choice):
     if model_choice == "Random Forest":
-        with open('rfweights (1).pkl', 'rb') as pickleFile:
+        with open('rfweights.pkl', 'rb') as pickleFile:
             return pickle.load(pickleFile)
     elif model_choice == "Decision Tree":
         with open('dtreeweights.pkl', 'rb') as pickleFile:
@@ -54,80 +50,41 @@ 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
-# def fetch_job_listings(job_title):
-#     url = "https://jobs-api14.p.rapidapi.com/v2/list"
-#     querystring = {
-#         "query": "software engineer",
-#         "location": "India",
-#         "autoTranslateLocation": "false",
-#         "remoteOnly": "false",
-#         "employmentTypes": "fulltime;parttime;intern;contractor"
-#     }
-#     headers = {
-#         "x-rapidapi-key": "47d14c1b58msh66e23d95e91b8bep110e5fjsn64ef19ff56c0",
-#         "x-rapidapi-host": "job-posting-feed-api.p.rapidapi.com"
-#     }
-
-#     try:
-#         response = requests.get(url, headers=headers, params=querystring)
-#         job_data = response.json()
-        
-#         # 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
-#         else:
-#             return [['No job listings', 'found', 'for this', 'career path']]
-    
-#     except requests.RequestException as e:
-#         return [['Error', 'fetching', 'job listings', str(e)]]
-
-
-
-import requests
-
+# Function to fetch job listings
 def fetch_job_listings(job_title):
-    url = "https://active-jobs-db.p.rapidapi.com/active-ats-7d"
-
-    params = {
-        "limit": "10",
-        "title_filter": job_title,
-        "location_filter": "United States",
-        "description_type": "text"
+    url = "https://jobs-api14.p.rapidapi.com/v2/list"
+    querystring = {
+        "query": job_title,
+        "location": "India",
+        "autoTranslateLocation": "false",
+        "remoteOnly": "false",
+        "employmentTypes": "fulltime;parttime;intern;contractor"
     }
-
     headers = {
-        "X-RapidAPI-Key": "47d14c1b58msh66e23d95e91b8bep110e5fjsn64ef19ff56c0",
-        "X-RapidAPI-Host": "active-jobs-db.p.rapidapi.com"
+        "x-rapidapi-key": "47d14c1b58msh66e23d95e91b8bep110e5fjsn64ef19ff56c0",
+        "x-rapidapi-host": "jobs-api14.p.rapidapi.com"
     }
 
-    response = requests.get(url, headers=headers, params=params)
-    job_data = response.json()
-
-    print("RAW RESPONSE:", job_data)
-
-    # ✅ Active Jobs DB returns a LIST, not dict
-    if isinstance(job_data, list) and len(job_data) > 0:
-        jobs = []
-        for job in job_data[:5]:
-            jobs.append([
-                job.get("title", "N/A"),
-                job.get("organization", "N/A"),
-                job.get("location", "N/A"),
-                job.get("salary_raw", "Not specified")
-            ])
-        return jobs
-    else:
-        return [["No job listings", "found", "for this", "career path"]]
-
+    try:
+        response = requests.get(url, headers=headers, params=querystring)
+        job_data = response.json()
+        
+        # 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
+        else:
+            return [['No job listings', 'found', 'for this', 'career path']]
+    
+    except requests.RequestException as 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,
@@ -277,8 +234,7 @@ demo = gr.Interface(
         gr.Dropdown(worker_list, label="Are you a Smart worker or Hard worker?")
     ],
     outputs=create_output_component(), 
-    title="AI-Enhanced Career guidance System",
-
+    title="Ai-Enhanced career guidance system"
 )
 
 # Main execution