ghj

app.py

@@ -84,86 +84,28 @@ def show_info(topic):
    }
    return responses.get(topic, "Select a category to see the corresponding careers.")
 
-# Resources Page
-def resource_block(career):
-   yt_videos = {
-       "AI/Machine Learning Engineer": '<iframe width="100%" height="315" src="https://www.youtube.com/embed/ukzFI9rgwfU" title="AI Career Advice" frameborder="0" allowfullscreen></iframe>',
-       "Data Scientist": '<iframe width="100%" height="315" src="https://www.youtube.com/embed/xC-c7E5PK0Y" title="Day in the Life of Data Scientist" frameborder="0" allowfullscreen></iframe>',
-       "Cloud Solutions Architect": '<iframe width="100%" height="315" src="https://www.youtube.com/embed/COgFYt6GxC4" title="Become a Cloud Architect" frameborder="0" allowfullscreen></iframe>'
-   }
-
-   resources = {
-       "AI/Machine Learning Engineer": {
-           "links": [
-               ("Neural Networks – DeepLearning.AI", "https://www.deeplearning.ai"),
-               ("Build ML Models – Fast.ai", "https://www.fast.ai"),
-               ("Machine Learning – Stanford CS229", "https://cs229.stanford.edu/")
-           ],
-           "college": {
-               "major": "Computer Science, Data Science",
-               "classes": [
-                   "CS50: Introduction to Computer Science (Harvard)",
-                   "Linear Algebra",
-                   "Probability and Statistics",
-                   "Machine Learning (Stanford CS229)",
-                   "Algorithms"
-               ]
-           }
-       },
-       "Data Scientist": {
-           "links": [
-               ("Python & Pandas – Kaggle Learn", "https://www.kaggle.com/learn"),
-               ("R Programming – Harvard Data Science", "https://online-learning.harvard.edu/series/data-science"),
-               ("Project Practice – DataCamp", "https://www.datacamp.com")
-           ],
-           "college": {
-               "major": "Data Science, Statistics, Computer Science",
-               "classes": [
-                   "Introduction to Data Science",
-                   "Statistics and Probability",
-                   "Data Mining",
-                   "Machine Learning",
-                   "Database Systems"
-               ]
-           }
-       },
-       "Cloud Solutions Architect": {
-           "links": [
-               ("AWS Skills – AWS Training", "https://aws.amazon.com/training/"),
-               ("Azure Certifications – Microsoft Learn", "https://learn.microsoft.com/en-us/certifications/"),
-               ("Google Cloud Labs – Google Cloud Boost", "https://cloudskillsboost.google/")
-           ],
-           "college": {
-               "major": "Computer Science, Information Technology",
-               "classes": [
-                   "Cloud Computing Fundamentals",
-                   "Computer Networks",
-                   "Systems Design",
-                   "Information Security",
-                   "Operating Systems"
-               ]
-           }
-       }
-   }
+# Load 20 career resource entries from external file or definition
+from resource_data import career_resources  # This file should define a dictionary named `career_resources`
 
-   content = resources.get(career)
+def resource_block(career):
+   content = career_resources.get(career)
    if not content:
        return "Select a career to see resources.", ""
 
-   link_html = "<h4>Skills</h4><ul>"
-   for label, url in content["links"]:
+   link_html = "<h4>\ud83e\udde0 Skills</h4><ul>"
+   for label, url in content["skills"]:
        link_html += f"<li><strong>{label}</strong>: <a href='{url}' target='_blank'>{url}</a></li>"
-   link_html += "</ul><br><hr><br>"
+   link_html += "</ul><br>"
 
-   college_html = "<h4>College & Classes</h4><ul>"
-   college_html += f"<li><strong>Majors:</strong> {content['college']['major']}</li>"
+   college_html = "<h4>\ud83c\udf93 College & Classes</h4><ul>"
+   college_html += f"<li><strong>Majors:</strong> {content['major']}</li>"
    college_html += "<li><strong>Helpful Classes:</strong><ul>"
-   for cls in content['college']['classes']:
-       college_html += f"<li>{cls}</li>"
-   college_html += "</ul></li></ul><br><hr><br>"
+   for course in content["classes"]:
+       college_html += f"<li>{course}</li>"
+   college_html += "</ul></li></ul><hr>"
 
-   video_embed = yt_videos.get(career, "")
-   return link_html + college_html, video_embed
+   video_embed = content.get("youtube", "")
+   return link_html + "<br>" + college_html, video_embed
 
 # UI Layout
 with gr.Blocks(theme=theme, css=custom_css) as chatbot:
@@ -196,11 +138,7 @@ with gr.Blocks(theme=theme, css=custom_css) as chatbot:
    with gr.Tab("Resources"):
        gr.Markdown("### Career-Specific Educational Resources")
        dropdown_resources = gr.Dropdown(
-           choices=[
-               "AI/Machine Learning Engineer",
-               "Data Scientist",
-               "Cloud Solutions Architect"
-           ],
+           choices=list(career_resources.keys()),
            label="Choose a Career"
        )
        output_links = gr.HTML()