Update app.py

app.py

@@ -1,222 +1,6 @@
 
 
 
-# import gradio as gr
-# import pandas as pd 
-# import numpy as np 
-# import pickle
-# import sklearn
-# from datasets import load_dataset
-# import joblib
-# import requests  
-
-
-# # Read the data
-# 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:
-#             return pickle.load(pickleFile)
-#     elif model_choice == "Decision Tree":
-#         with open('dtreeweights.pkl', 'rb') as pickleFile:
-#             return pickle.load(pickleFile)
-   
-#     else:
-#         raise ValueError("Invalid model selection")
-
-# # Prepare categorical data (same as original code)
-# categorical_cols = data[[
-#     'certifications',
-#     'workshops',
-#     'Interested subjects',
-#     'interested career area ',
-#     'Type of company want to settle in?',
-#     'Interested Type of Books'
-# ]]
-
-# # Assign category codes
-# for i in categorical_cols:
-#     data[i] = data[i].astype('category')
-#     data[i] = data[i].cat.codes
-
-# # Create reference dictionaries for embeddings (same as original code)
-# def create_embedding_dict(column):
-#     unique_names = list(categorical_cols[column].unique())
-#     unique_codes = list(data[column].unique())
-#     return dict(zip(unique_names, unique_codes))
-
-# certificates_references = create_embedding_dict('certifications')
-# workshop_references = create_embedding_dict('workshops')
-# subjects_interest_references = create_embedding_dict('Interested subjects')
-# 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')
-
-# # Prediction function (modified to accept model choice)
-# def rfprediction(model_choice, name, logical_thinking, hackathon_attend, coding_skills, public_speaking_skills,
-#                  self_learning, extra_course, certificate_code, worskhop_code, read_writing_skill, memory_capability,
-#                  subject_interest, career_interest, company_intend, senior_elder_advise, book_interest, introvert_extro,
-#                  team_player, management_technical, smart_hardworker):
-#     # Load the selected model
-#     rfmodel = load_model(model_choice)
-    
-#     # Create DataFrame (same as original code)
-#     df = pd.DataFrame.from_dict(
-#         {
-#             "logical_thinking": [logical_thinking],
-#             "hackathon_attend": [hackathon_attend],
-#             "coding_skills": [coding_skills],
-#             "public_speaking_skills": [public_speaking_skills],
-#             "self_learning": [self_learning],
-#             "extra_course": [extra_course],
-#             "certificate": [certificate_code],
-#             "workshop": [worskhop_code],
-#             "read_writing_skills": [
-#                     (0 if "poor" in read_writing_skill else 1 if "medium" in read_writing_skill else 2)
-#                 ],
-#             "memory_capability": [
-#                     (0 if "poor" in memory_capability else 1 if "medium" in memory_capability else 2)   
-#                 ],
-#             "subject_interest": [subject_interest],
-#             "career_interest": [career_interest],
-#             "company_intend": [company_intend],
-#             "senior_elder_advise": [senior_elder_advise],
-#             "book_interest": [book_interest],
-#             "introvert_extro": [introvert_extro],
-#             "team_player": [team_player],
-#             "management_technical":[management_technical],
-#             "smart_hardworker": [smart_hardworker]
-#         }
-#     )
-    
-#     # Replace string values with numeric representations
-#     df = df.replace({
-#         "certificate": certificates_references, 
-#         "workshop": workshop_references,
-#         "subject_interest": subjects_interest_references,
-#         "career_interest": career_interest_references,
-#         "company_intend": company_intends_references,
-#         "book_interest": book_interest_references
-#     })
-    
-#     # Dummy encoding (same as original code)
-#     userdata_list = df.values.tolist()
-    
-#     # Management-Technical dummy encoding
-#     if(df["management_technical"].values == "Management"):
-#         userdata_list[0].extend([1])
-#         userdata_list[0].extend([0])
-#         userdata_list[0].remove('Management')
-#     elif(df["management_technical"].values == "Technical"):
-#         userdata_list[0].extend([0])
-#         userdata_list[0].extend([1])
-#         userdata_list[0].remove('Technical')
-#     else: 
-#         return "Error in Management-Technical encoding"
-
-#     # Smart-Hard worker dummy encoding
-#     if(df["smart_hardworker"].values == "smart worker"):
-#         userdata_list[0].extend([1])
-#         userdata_list[0].extend([0])
-#         userdata_list[0].remove('smart worker')
-#     elif(df["smart_hardworker"].values == "hard worker"):
-#         userdata_list[0].extend([0])
-#         userdata_list[0].extend([1])
-#         userdata_list[0].remove('hard worker')
-#     else: 
-#         return "Error in Smart-Hard worker encoding"
-
-#     # Prediction
-#     prediction_result_all = rfmodel.predict_proba(userdata_list)
-    
-#     # Create result dictionary
-#     result_list = {
-#         "Applications Developer": float(prediction_result_all[0][0]),
-#         "CRM Technical Developer": float(prediction_result_all[0][1]),
-#         "Database Developer": float(prediction_result_all[0][2]),
-#         "Mobile Applications Developer": float(prediction_result_all[0][3]),
-#         "Network Security Engineer": float(prediction_result_all[0][4]),
-#         "Software Developer": float(prediction_result_all[0][5]),
-#         "Software Engineer": float(prediction_result_all[0][6]),
-#         "Software Quality Assurance (QA)/ Testing": float(prediction_result_all[0][7]),
-#         "Systems Security Administrator": float(prediction_result_all[0][8]),
-#         "Technical Support": float(prediction_result_all[0][9]),
-#         "UX Designer": float(prediction_result_all[0][10]),
-#         "Web Developer": float(prediction_result_all[0][11]),
-#     }
-#     return result_list
-
-# # Lists for dropdown menus (same as original code)
-# cert_list = ["app development", "distro making", "full stack", "hadoop", "information security", "machine learning", "python", "r programming", "shell programming"]
-# workshop_list = ["cloud computing", "data science", "database security", "game development", "hacking", "system designing", "testing", "web technologies"]
-# skill = ["excellent", "medium", "poor"]
-# subject_list = ["cloud computing", "Computer Architecture", "data engineering", "hacking", "IOT", "Management", "networks", "parallel computing", "programming", "Software Engineering"]
-# career_list = ["Business process analyst", "cloud computing", "developer", "security", "system developer", "testing"]
-# company_list = ["BPA", "Cloud Services", "Finance", "Product based", "product development", "SAaS services", "Sales and Marketing", "Service Based", "Testing and Maintainance Services", "Web Services"]
-# book_list = ["Action and Adventure", "Anthology", "Art", "Autobiographies", "Biographies", "Childrens", "Comics","Cookbooks","Diaries","Dictionaries","Drama","Encyclopedias","Fantasy","Guide","Health","History","Horror","Journals","Math","Mystery","Poetry","Prayer books","Religion-Spirituality","Romance","Satire","Science","Science fiction","Self help","Series","Travel","Trilogy"]
-# Choice_list = ["Management", "Technical"]
-# worker_list = ["hard worker", "smart worker"]
-
-# # Create Gradio interface (modified to include model selection)
-# demo = gr.Interface(
-#     fn=rfprediction, 
-#     inputs=[
-#         gr.Dropdown(["Random Forest", "Decision Tree"], label="Select Machine Learning Model"),
-#         gr.Textbox(placeholder="What is your name?", label="Name"),
-#         gr.Slider(minimum=1, maximum=9, value=3, step=1, label="Are you a logical thinking person?", info="Scale: 1 - 9"),
-#         gr.Slider(minimum=0, maximum=6, value=0, step=1, label="Do you attend any Hackathons?", info="Scale: 0 - 6 | 0 - if not attended any"),
-#         gr.Slider(minimum=1, maximum=9, value=5, step=1, label="How do you rate your coding skills?", info="Scale: 1 - 9"),
-#         gr.Slider(minimum=1, maximum=9, value=3, step=1, label="How do you rate your public speaking skills/confidency?", info="Scale: 1 - 9"),
-#         gr.Radio({"Yes", "No"}, type="index", label="Are you a self-learning person? *"),
-#         gr.Radio({"Yes", "No"}, type="index", label="Do you take extra courses in uni (other than IT)? *"),
-#         gr.Dropdown(cert_list, label="Select a certificate you took!"),
-#         gr.Dropdown(workshop_list, label="Select a workshop you attended!"),
-#         gr.Dropdown(skill, label="Select your read and writing skill"),
-#         gr.Dropdown(skill, label="Is your memory capability good?"),
-#         gr.Dropdown(subject_list, label="What subject you are interested in?"),
-#         gr.Dropdown(career_list, label="Which IT-Career do you have interests in?"),
-#         gr.Dropdown(company_list, label="Do you have any interested company that you intend to settle in?"),
-#         gr.Radio({"Yes", "No"}, type="index", label="Do you ever seek any advices from senior or elders? *"),
-#         gr.Dropdown(book_list, label="Select your interested genre of book!"),
-#         gr.Radio({"Yes", "No"}, type="index", label="Are you an Introvert?| No - extrovert *"),
-#         gr.Radio({"Yes", "No"}, type="index", label="Ever worked in a team? *"),
-#         gr.Dropdown(Choice_list, label="Which area do you prefer: Management or Technical?"),
-#         gr.Dropdown(worker_list, label="Are you a Smart worker or Hard worker?")
-#     ],
-#     outputs=gr.Label(num_top_classes=5), 
-#     title="IT-Career Recommendation System: TMI4033 Colletive Intelligence, Group 12",
-#     description="Members: Derrick Lim Kin Yeap 74597, Jason Jong Sheng Tat 75125, Jason Ng Yong Xing 75127, Muhamad Hazrie Bin Suhkery 73555 "
-# )
-
-# url = "https://jobs-api14.p.rapidapi.com/v2/list"
-# querystring = {
-#     "query":"Web Developer",
-#     "location":"India",
-#     "autoTranslateLocation":"false",
-#     "remoteOnly":"false",
-#     "employmentTypes":"fulltime;parttime;intern;contractor"
-# }
-# headers = {
-#     "x-rapidapi-key": "714f5a2539msh798d996c3243876p19c71ajsnfcd7ce481cb9",
-#     "x-rapidapi-host": "jobs-api14.p.rapidapi.com"
-# }
-
-
-# # Main execution
-# if __name__ == "__main__":
-#     # Fetch job listings before launching the app
-#     try:
-#         response = requests.get(url, headers=headers, params=querystring)
-#         job_listings = response.json()
-#         print("Job Listings Retrieved Successfully")
-#         # You could potentially store or process job_listings here
-#     except requests.RequestException as e:
-#         print(f"Error fetching job listings: {e}")
-    
-#     demo.launch(share=True)
-
 
 import gradio as gr
 import pandas as pd 
@@ -319,28 +103,29 @@ def fetch_job_listings(job_title):
     }
 
     headers = {
-        "X-RapidAPI-Key": "47d14c1b58msh66e23d95e91b8bep110e5fjsn64ef19ff56c0",
+        "X-RapidAPI-Key": "YOUR_NEW_API_KEY",
         "X-RapidAPI-Host": "jobs-api14.p.rapidapi.com"
     }
 
     response = requests.get(url, headers=headers, params=querystring)
     job_data = response.json()
 
-    print("RAW RESPONSE:", job_data)  # DEBUG (keep this for now)
+    print("RAW RESPONSE:", job_data)  # keep for now
 
-    if job_data.get("jobs"):
+    if job_data.get("data"):
         job_listings = []
-        for job in job_data["jobs"][:5]:
+        for job in job_data["data"][:5]:
             job_listings.append([
-                job.get("title", "N/A"),
-                job.get("company", "N/A"),
-                job.get("location", "N/A"),
-                job.get("salary", "Not specified")
+                job.get("job_title", "N/A"),
+                job.get("employer_name", "N/A"),
+                job.get("job_city", "N/A"),
+                job.get("job_min_salary", "Not specified")
             ])
         return job_listings
     else:
         return [["No job listings", "found", "for this", "career path"]]
 
+
 # Prediction function (modified to return job suggestions)
 def rfprediction(model_choice, name, logical_thinking, hackathon_attend, coding_skills, public_speaking_skills,
                  self_learning, extra_course, certificate_code, worskhop_code, read_writing_skill, memory_capability,