update

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 from sentence_transformers import SentenceTransformer
-import torch 
+import torch
 
 # Theme
 theme = gr.themes.Soft(
@@ -12,10 +12,10 @@ theme = gr.themes.Soft(
 
 custom_css = """
 :root {
-    --background-fill-primary: #FFB6C2 !important;
+   --background-fill-primary: #FFB6C2 !important;
 }
 .dark {
-    --background-fill-primary: #FFB6C1 !important;
+   --background-fill-primary: #FFB6C1 !important;
 }
 """
 
@@ -63,7 +63,6 @@ def respond(message, history):
     if history:
         messages.extend(history)
     messages.append({'role': 'user', 'content': message})
-
     response = client.chat_completion(
         messages,
         max_tokens=1000,
@@ -79,31 +78,93 @@ def show_info(topic):
     responses = {
         "Highest Paying STEM Jobs": "1. AI/Machine Learning Engineer – $171,774\n2. Cloud Solutions Architect – $150,241\n3. Quantitative Analyst (Quant) – $139,949\n4. Data Scientist – $128,115\n5. Actuary – $128,147",
         "Most Flexible STEM Jobs": "1. Software Developer\n2. Cloud Solutions Architect\n3. Data Scientist\n4. Cybersecurity Analyst\n5. Statistician",
-        "Most Creative STEM Jobs": "1. Software Developer\n2. AI/Machine Learning Engineer\n3. Biomedical Engineer\n4. Mechanical Engineer\n5. Biochemist", 
-        "Fastest Growing STEM Jobs": "1. AI/Machine Learning Engineer\n2. Cybersecurity Analyst\n3. Data Scientist\n4. Software Developer\n5. Cloud Solutions Architect", 
+        "Most Creative STEM Jobs": "1. Software Developer\n2. AI/Machine Learning Engineer\n3. Biomedical Engineer\n4. Mechanical Engineer\n5. Biochemist",
+        "Fastest Growing STEM Jobs": "1. AI/Machine Learning Engineer\n2. Cybersecurity Analyst\n3. Data Scientist\n4. Software Developer\n5. Cloud Solutions Architect",
         "Low-Stress STEM Jobs": "1. Statistician\n2. Mathematician\n3. Operations Research Analyst\n4. Environmental Scientist\n5. Biochemist"
     }
     return responses.get(topic, "Select a category to see the corresponding careers.")
 
-# Resources Page Info - UPDATED
+# Resources Page
 def resource_block(career):
     yt_videos = {
-        "AI/Machine Learning Engineer": "https://www.youtube.com/embed/ukzFI9rgwfU",
-        "Data Scientist": "https://www.youtube.com/embed/xC-c7E5PK0Y",
-        "Cloud Solutions Architect": "https://www.youtube.com/embed/l1EssrLxt7E"
+        "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"
+                ]
+            }
+        }
     }
 
-    # Reuse existing function to get resources
-    from app import resource_block as base_resource_block
-    text_html, _ = base_resource_block(career)
+    content = resources.get(career)
+    if not content:
+        return "Select a career to see resources.", ""
 
-    video_url = yt_videos.get(career)
-    if video_url:
-        video_html = f'<iframe width="100%" height="315" src="{video_url}" frameborder="0" allowfullscreen></iframe>'
-    else:
-        video_html = ""
+    link_html = "<h4>🧠 Skills & Learning Resources</h4><ul>"
+    for label, url in content["links"]:
+        link_html += f"<li><strong>{label}</strong>: <a href='{url}' target='_blank'>{url}</a></li>"
+    link_html += "</ul><br>"
 
-    return text_html, video_html
+    college_html = ""
+    if "college" in content:
+        college = content["college"]
+        college_html += "<h4>🎓 College & Classes</h4><ul>"
+        college_html += f"<li><strong>Majors:</strong> {college['major']}</li>"
+        college_html += f"<li><strong>Helpful Classes:</strong> {', '.join(college['classes'])}</li>"
+        college_html += "</ul><br>"
+
+    video_embed = yt_videos.get(career, "")
+    return link_html + college_html, video_embed
 
 # UI Layout
 with gr.Blocks(theme=theme, css=custom_css) as chatbot:
@@ -139,37 +200,12 @@ with gr.Blocks(theme=theme, css=custom_css) as chatbot:
             choices=[
                 "AI/Machine Learning Engineer",
                 "Data Scientist",
-                "Cloud Solutions Architect",
-                "Cybersecurity Analyst",
-                "Statistician",
-                "Biomedical Engineer",
-                "Mechanical Engineer",
-                "Environmental Scientist",
-                "Operations Research Analyst",
-                "Mathematician",
-                "Chemical Engineer",
-                "Civil Engineer",
-                "Electrical Engineer",
-                "Software Developer",
-                "Pharmacist",
-                "Physicist",
-                "Astronomer",
-                "Geologist",
-                "Biochemist"
+                "Cloud Solutions Architect"
             ],
             label="Choose a Career"
         )
         output_links = gr.HTML()
         output_video = gr.HTML()
-        dropdown_resources.change(
-            fn=resource_block,
-            inputs=dropdown_resources,
-            outputs=[output_links, output_video]
-        )
+        dropdown_resources.change(fn=resource_block, inputs=dropdown_resources, outputs=[output_links, output_video])
 
 chatbot.launch()
-
-
-
-            
-