update

app.py

@@ -5,30 +5,30 @@ import torch
 
 # Theme
 theme = gr.themes.Soft(
-    primary_hue="rose",
-    secondary_hue="zinc",
-    neutral_hue="pink"
+   primary_hue="rose",
+   secondary_hue="zinc",
+   neutral_hue="pink"
 )
 
 custom_css = """
 :root {
-   --background-fill-primary: #FFB6C2 !important;
+  --background-fill-primary: #FFB6C2 !important;
 }
 .dark {
-   --background-fill-primary: #FFB6C1 !important;
+  --background-fill-primary: #FFB6C1 !important;
 }
 """
 
 # Load research file
 with open("research.txt", "r", encoding="utf-8") as file:
-    research_text = file.read()
+   research_text = file.read()
 
 # Preprocess text
 def preprocess_text(text):
-    cleaned_text = text.strip()
-    chunks = cleaned_text.split("\n")
-    cleaned_chunks = [chunk.strip() for chunk in chunks if chunk.strip() != ""]
-    return cleaned_chunks
+   cleaned_text = text.strip()
+   chunks = cleaned_text.split("\n")
+   cleaned_chunks = [chunk.strip() for chunk in chunks if chunk.strip() != ""]
+   return cleaned_chunks
 
 cleaned_chunks = preprocess_text(research_text)
 
@@ -36,444 +36,175 @@ cleaned_chunks = preprocess_text(research_text)
 model = SentenceTransformer('all-MiniLM-L6-v2')
 
 def create_embeddings(text_chunks):
-    chunk_embeddings = model.encode(text_chunks, convert_to_tensor=True)
-    return chunk_embeddings
+   chunk_embeddings = model.encode(text_chunks, convert_to_tensor=True)
+   return chunk_embeddings
 
 chunk_embeddings = create_embeddings(cleaned_chunks)
 
 # Get top chunks
 def get_top_chunks(query, chunk_embeddings, text_chunks):
-    query_embedding = model.encode(query, convert_to_tensor=True)
-    query_embedding_normalized = query_embedding / query_embedding.norm()
-    chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True)
-    similarities = torch.matmul(chunk_embeddings_normalized, query_embedding_normalized)
-    top_indices = torch.topk(similarities, k=3).indices
-    top_chunks = [text_chunks[i] for i in top_indices]
-    return top_chunks
+   query_embedding = model.encode(query, convert_to_tensor=True)
+   query_embedding_normalized = query_embedding / query_embedding.norm()
+   chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True)
+   similarities = torch.matmul(chunk_embeddings_normalized, query_embedding_normalized)
+   top_indices = torch.topk(similarities, k=3).indices
+   top_chunks = [text_chunks[i] for i in top_indices]
+   return top_chunks
 
 # Inference client
 client = InferenceClient("Qwen/Qwen2.5-72B-Instruct")
 
 def respond(message, history):
-    top_results = get_top_chunks(message, chunk_embeddings, cleaned_chunks)
-    str_top_results = '\n'.join(top_results)
-    messages = [
-        {'role': 'system', 'content': f'You are a chatbot. Complete all your sentences, do not be blunt, and do not cut yourself off. The word limit is 100 words. Start off by giving a career in a complete, kind sentence, and then if prompted by the user provide more information like salary, college course,etc. Base your response on the provided context:\n{str_top_results}'}
-    ]
-    if history:
-        messages.extend(history)
-    messages.append({'role': 'user', 'content': message})
-    response = client.chat_completion(
-        messages,
-        max_tokens=1000,
-        temperature=0.2
-    )
-    return response['choices'][0]['message']['content'].strip()
+   top_results = get_top_chunks(message, chunk_embeddings, cleaned_chunks)
+   str_top_results = '\n'.join(top_results)
+   messages = [
+       {'role': 'system', 'content': f'You are a chatbot. Complete all your sentences, do not be blunt, and do not cut yourself off. The word limit is 100 words. Start off by giving a career in a complete, kind sentence, and then if prompted by the user provide more information like salary, college course,etc. Base your response on the provided context:\n{str_top_results}'}
+   ]
+   if history:
+       messages.extend(history)
+   messages.append({'role': 'user', 'content': message})
+   response = client.chat_completion(
+       messages,
+       max_tokens=1000,
+       temperature=0.2
+   )
+   return response['choices'][0]['message']['content'].strip()
 
 def display_image():
-    return "KWKbanner.png"
+   return "KWKbanner.png"
 
 # Explore Page Info
 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",
-        "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.")
+   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",
+       "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
 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"
-                ]
-            }
-        },
-        "Cybersecurity Analyst": {
-            "links": [
-                ("Network Security – Cybrary", "https://www.cybrary.it"),
-                ("Threat Intelligence – MITRE ATT&CK", "https://attack.mitre.org/"),
-                ("Ethical Hacking – TryHackMe", "https://tryhackme.com")
-            ],
-            "college": {
-                "major": "Cybersecurity, Computer Science, Information Security",
-                "classes": [
-                    "Network Security",
-                    "Cryptography",
-                    "Ethical Hacking",
-                    "Operating Systems",
-                    "Incident Response"
-                ]
-            }
-        },
-        "Statistician": {
-            "links": [
-                ("Intro to Statistics – Coursera (R)", "https://www.coursera.org/specializations/statistics"),
-                ("Probability – Khan Academy", "https://www.khanacademy.org/math/statistics-probability"),
-                ("Statistical Tools – OpenIntro", "https://www.openintro.org/book/os/")
-            ],
-            "college": {
-                "major": "Statistics, Mathematics",
-                "classes": [
-                    "Probability Theory",
-                    "Statistical Inference",
-                    "Regression Analysis",
-                    "Experimental Design",
-                    "Data Analysis with R"
-                ]
-            }
-        },
-        "Biomedical Engineer": {
-            "links": [
-                ("Biomedical Research – JHU BME", "https://www.bme.jhu.edu/"),
-                ("Medical Devices – edX Courses", "https://www.edx.org/learn/biomedical-engineering"),
-                ("Clinical Trials – NIH", "https://www.nih.gov/")
-            ],
-            "college": {
-                "major": "Biomedical Engineering, Bioengineering",
-                "classes": [
-                    "Biomaterials",
-                    "Human Physiology",
-                    "Medical Instrumentation",
-                    "Biomechanics",
-                    "Tissue Engineering"
-                ]
-            }
-        },
-        "Mechanical Engineer": {
-            "links": [
-                ("CAD Design – Coursera", "https://www.coursera.org/learn/cad-design"),
-                ("Thermodynamics – MIT OpenCourseWare", "https://ocw.mit.edu/courses/thermodynamics"),
-                ("Materials Science Basics – edX", "https://www.edx.org/course/material-science")
-            ],
-            "college": {
-                "major": "Mechanical Engineering",
-                "classes": [
-                    "Thermodynamics",
-                    "Fluid Mechanics",
-                    "Materials Science",
-                    "Computer-Aided Design (CAD)",
-                    "Dynamics and Control"
-                ]
-            }
-        },
-        "Environmental Scientist": {
-            "links": [
-                ("Environmental Science – Khan Academy", "https://www.khanacademy.org/science/biology/ecology"),
-                ("GIS Basics – Esri Training", "https://www.esri.com/training/catalog/57630435851d31e02a43f1c5/gis-basics/"),
-                ("Data Analysis – Coursera", "https://www.coursera.org/learn/data-analysis")
-            ],
-            "college": {
-                "major": "Environmental Science, Ecology",
-                "classes": [
-                    "Ecology",
-                    "Environmental Chemistry",
-                    "Geographic Information Systems (GIS)",
-                    "Data Analysis",
-                    "Environmental Policy"
-                ]
-            }
-        },
-        "Operations Research Analyst": {
-            "links": [
-                ("Linear Programming – Khan Academy", "https://www.khanacademy.org/computing/computer-science/algorithms"),
-                ("Optimization – MIT OpenCourseWare", "https://ocw.mit.edu/courses/optimization-methods"),
-                ("Statistics – Harvard Online", "https://online-learning.harvard.edu/course/statistics-and-r")
-            ],
-            "college": {
-                "major": "Operations Research, Applied Mathematics",
-                "classes": [
-                    "Optimization Theory",
-                    "Linear Programming",
-                    "Probability",
-                    "Statistics",
-                    "Simulation Modeling"
-                ]
-            }
-        },
-        "Mathematician": {
-            "links": [
-                ("Abstract Algebra – MIT OpenCourseWare", "https://ocw.mit.edu/courses/abstract-algebra"),
-                ("Calculus – Khan Academy", "https://www.khanacademy.org/math/calculus-1"),
-                ("Proof Techniques – Coursera", "https://www.coursera.org/learn/proofs")
-            ],
-            "college": {
-                "major": "Mathematics",
-                "classes": [
-                    "Algebra",
-                    "Calculus",
-                    "Real Analysis",
-                    "Abstract Algebra",
-                    "Proof Writing"
-                ]
-            }
-        },
-        "Chemical Engineer": {
-            "links": [
-                ("Chemical Process Principles – MIT OCW", "https://ocw.mit.edu/courses/chemical-engineering"),
-                ("Organic Chemistry – Khan Academy", "https://www.khanacademy.org/science/organic-chemistry"),
-                ("Thermodynamics – Coursera", "https://www.coursera.org/learn/thermodynamics")
-            ],
-            "college": {
-                "major": "Chemical Engineering",
-                "classes": [
-                    "Organic Chemistry",
-                    "Thermodynamics",
-                    "Process Design",
-                    "Fluid Mechanics",
-                    "Chemical Reaction Engineering"
-                ]
-            }
-        },
-        "Civil Engineer": {
-            "links": [
-                ("Structural Analysis – Coursera", "https://www.coursera.org/learn/structural-analysis"),
-                ("Construction Management – edX", "https://www.edx.org/course/construction-management"),
-                ("AutoCAD – LinkedIn Learning", "https://www.linkedin.com/learning/topics/autocad")
-            ],
-            "college": {
-                "major": "Civil Engineering",
-                "classes": [
-                    "Structural Analysis",
-                    "Construction Materials",
-                    "Soil Mechanics",
-                    "AutoCAD",
-                    "Hydraulics"
-                ]
-            }
-        },
-        "Electrical Engineer": {
-            "links": [
-                ("Circuits and Electronics – MIT OCW", "https://ocw.mit.edu/courses/electrical-engineering-and-computer-science"),
-                ("Signals and Systems – Coursera", "https://www.coursera.org/learn/signals-systems"),
-                ("Electromagnetics – Khan Academy", "https://www.khanacademy.org/science/electrical-engineering")
-            ],
-            "college": {
-                "major": "Electrical Engineering",
-                "classes": [
-                    "Circuits",
-                    "Signals and Systems",
-                    "Electromagnetics",
-                    "Control Systems",
-                    "Digital Logic Design"
-                ]
-            }
-        },
-        "Software Developer": {
-            "links": [
-                ("CS50 – Harvard", "https://cs50.harvard.edu"),
-                ("Learn to Code – Codecademy", "https://www.codecademy.com/catalog/subject/all"),
-                ("Algorithms – Coursera", "https://www.coursera.org/learn/algorithms-part1")
-            ],
-            "college": {
-                "major": "Computer Science, Software Engineering",
-                "classes": [
-                    "Introduction to Computer Science (CS50)",
-                    "Data Structures and Algorithms",
-                    "Operating Systems",
-                    "Software Engineering",
-                    "Databases"
-                ]
-            }
-        },
-        "Pharmacist": {
-            "links": [
-                ("Pharmacology Basics – Coursera", "https://www.coursera.org/learn/pharmacology"),
-                ("Drug Development – edX", "https://www.edx.org/course/drug-development"),
-                ("Pharmacy Practice – FutureLearn", "https://www.futurelearn.com/courses/pharmacy-practice")
-            ],
-            "college": {
-                "major": "Pharmacy, Pharmaceutical Sciences",
-                "classes": [
-                    "Pharmacology",
-                    "Medicinal Chemistry",
-                    "Pharmaceutical Calculations",
-                    "Pharmaceutics",
-                    "Clinical Pharmacy"
-                ]
-            }
-        },
-        "Physicist": {
-            "links": [
-                ("Classical Mechanics – MIT OCW", "https://ocw.mit.edu/courses/physics"),
-                ("Quantum Mechanics – edX", "https://www.edx.org/course/quantum-mechanics"),
-                ("Thermodynamics – Khan Academy", "https://www.khanacademy.org/science/physics/thermodynamics")
-            ],
-            "college": {
-                "major": "Physics",
-                "classes": [
-                    "Classical Mechanics",
-                    "Quantum Mechanics",
-                    "Thermodynamics",
-                    "Electromagnetism",
-                    "Mathematical Methods for Physicists"
-                ]
-            }
-        },
-        "Astronomer": {
-            "links": [
-                ("Introduction to Astronomy – Coursera", "https://www.coursera.org/learn/astronomy"),
-                ("Astrophysics – edX", "https://www.edx.org/course/astrophysics"),
-                ("Cosmology – Khan Academy", "https://www.khanacademy.org/science/cosmology-and-astronomy")
-            ],
-            "college": {
-                "major": "Astronomy, Astrophysics, Physics",
-                "classes": [
-                    "Introduction to Astronomy",
-                    "Astrophysics",
-                    "Cosmology",
-                    "Observational Astronomy",
-                    "Data Analysis in Astronomy"
-                ]
-            }
-        },
-        "Geologist": {
-            "links": [
-                ("Physical Geology – OpenStax", "https://openstax.org/details/books/physical-geology"),
-                ("Geochemistry – Coursera", "https://www.coursera.org/learn/geochemistry"),
-                ("GIS Mapping – Esri Training", "https://www.esri.com/training/catalog/57630435851d31e02a43f1c5/gis-basics/")
-            ],
-            "college": {
-                "major": "Geology, Earth Science",
-                "classes": [
-                    "Physical Geology",
-                    "Mineralogy and Petrology",
-                    "Geochemistry",
-                    "GIS and Remote Sensing",
-                    "Structural Geology"
-                ]
-            }
-        },
-        "Biochemist": {
-            "links": [
-                ("Biochemistry – MIT OCW", "https://ocw.mit.edu/courses/biochemistry"),
-                ("Molecular Biology – Coursera", "https://www.coursera.org/learn/molecular-biology"),
-                ("Enzymology – Khan Academy", "https://www.khanacademy.org/science/biology")
-            ],
-            "college": {
-                "major": "Biochemistry, Molecular Biology",
-                "classes": [
-                    "General Biochemistry",
-                    "Molecular Biology",
-                    "Enzymology",
-                    "Cell Biology",
-                    "Genetics"
-                ]
-            }
-        }
-    }
-
-    content = resources.get(career)
-    if not content:
-        return "Select a career to see resources.", ""
-
-    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>"
-
-    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
+   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"
+               ]
+           }
+       }
+   }
+
+   content = 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 += f"<li><strong>{label}</strong>: <a href='{url}' target='_blank'>{url}</a></li>"
+   link_html += "</ul><br><hr><br>"
+
+   college_html = "<h4>College & Classes</h4><ul>"
+   college_html += f"<li><strong>Majors:</strong> {content['college']['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>"
+
+   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:
-    gr.Image(display_image)
-
-    with gr.Tab("ChatBot"):
-        gr.ChatInterface(
-            respond,
-            type="messages",
-            title="Hi, I'm Path Pilot!",
-            textbox=gr.Textbox(placeholder="Share your interests and explore more on your career of choice!"),
-            description='This tool provides information on STEM Careers. All information is sourced from [census.gov](https://www.census.gov/).'
-        )
-
-    with gr.Tab("Explore Now"):
-        gr.Markdown("### Explore STEM Career Categories")
-        dropdown_explore = gr.Dropdown(
-            choices=[
-                "Highest Paying STEM Jobs",
-                "Most Flexible STEM Jobs",
-                "Most Creative STEM Jobs",
-                "Fastest Growing STEM Jobs",
-                "Low-Stress STEM Jobs"
-            ],
-            label="Choose a Category"
-        )
-        output_explore = gr.Markdown()
-        dropdown_explore.change(fn=show_info, inputs=dropdown_explore, outputs=output_explore)
-
-    with gr.Tab("Resources"):
-        gr.Markdown("### Career-Specific Educational Resources")
-        dropdown_resources = gr.Dropdown(
-            choices=list(resources.keys()),
-            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])
+   gr.Image(display_image)
+
+   with gr.Tab("ChatBot"):
+       gr.ChatInterface(
+           respond,
+           type="messages",
+           title="Hi, I'm Path Pilot!",
+           textbox=gr.Textbox(placeholder="Share your interests and explore more on your career of choice!"),
+           description='This tool provides information on STEM Careers. All information is sourced from [census.gov](https://www.census.gov/).'
+       )
+
+   with gr.Tab("Explore Now"):
+       gr.Markdown("### Explore STEM Career Categories")
+       dropdown_explore = gr.Dropdown(
+           choices=[
+               "Highest Paying STEM Jobs",
+               "Most Flexible STEM Jobs",
+               "Most Creative STEM Jobs",
+               "Fastest Growing STEM Jobs",
+               "Low-Stress STEM Jobs"
+           ],
+           label="Choose a Category"
+       )
+       output_explore = gr.Markdown()
+       dropdown_explore.change(fn=show_info, inputs=dropdown_explore, outputs=output_explore)
+
+   with gr.Tab("Resources"):
+       gr.Markdown("### Career-Specific Educational Resources")
+       dropdown_resources = gr.Dropdown(
+           choices=[
+               "AI/Machine Learning Engineer",
+               "Data Scientist",
+               "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])
 
 chatbot.launch()