Spaces:
Sleeping
Sleeping
File size: 25,862 Bytes
9f54a3b 71ec4a8 9f54a3b 0e00146 a645649 c50f71f 866f7a8 9d150a9 866f7a8 c7e3a36 8041476 a9c7401 91d2c85 8041476 91d2c85 8041476 91d2c85 8041476 91d2c85 8041476 91d2c85 8041476 91d2c85 8041476 91d2c85 8041476 91d2c85 8041476 91d2c85 8041476 91d2c85 8041476 91d2c85 c7e3a36 8041476 c7e3a36 8041476 c7e3a36 91d2c85 8041476 7f8c6d6 8041476 71ec4a8 8041476 71ec4a8 8041476 866f7a8 8041476 866f7a8 8041476 866f7a8 8041476 866f7a8 df288fd 7fcff87 8041476 a645649 8041476 a645649 8041476 df288fd 8041476 c7e3a36 8041476 86ccd3b 8041476 c7e3a36 8041476 a645649 8041476 866f7a8 8041476 c7e3a36 8041476 df288fd 8041476 2306090 8041476 91ad748 8041476 91ad748 8041476 91ad748 2306090 91ad748 8041476 91ad748 8041476 91ad748 8041476 91ad748 8041476 91ad748 866f7a8 8041476 df288fd 8041476 86ccd3b 8041476 df288fd 8041476 df288fd 8041476 866f7a8 8041476 df288fd 8041476 6df0402 8041476 df288fd 8041476 df288fd 8041476 86ccd3b 8041476 9ad5122 8041476 df288fd 8041476 86ccd3b 8041476 86ccd3b 8041476 65eab1d 8041476 df288fd 8041476 91d2c85 6df0402 8041476 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 |
import streamlit as st
import requests
import os
import json
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import time
from datetime import datetime, timedelta
import random
import nltk
from nltk.corpus import stopwords
from wordcloud import WordCloud
import matplotlib.pyplot as plt
from streamlit_lottie import st_lottie
import base64
from PIL import Image
import io
# Download NLTK data
nltk.download('stopwords', quiet=True)
nltk.download('punkt', quiet=True)
# Set page config
st.set_page_config(page_title="DSEAP", page_icon="๐", layout="wide")
# Custom CSS for styling
st.markdown("""
<style>
@import url('https://fonts.googleapis.com/css2?family=Roboto:wght@300;400;700&display=swap');
.stApp {
background: #f0f2f6;
font-family: 'Roboto', sans-serif;
}
.header {
font-size: 42px;
font-weight: bold;
color: #1e3799;
text-align: center;
margin-bottom: 30px;
text-shadow: 2px 2px 4px rgba(0,0,0,0.1);
}
.subheader {
font-size: 28px;
font-weight: bold;
color: #3c6382;
text-align: center;
margin-bottom: 25px;
}
.section {
background: white;
padding: 25px;
border-radius: 15px;
box-shadow: 0px 8px 16px rgba(0, 0, 0, 0.1);
margin-bottom: 30px;
}
.footer {
text-align: center;
font-size: 14px;
color: #555;
margin-top: 30px;
padding: 10px;
border-top: 1px solid #ddd;
}
.stProgress > div > div > div > div {
background-image: linear-gradient(to right, #4a69bd, #6a89cc);
}
.stButton>button {
background-color: #4a69bd;
color: white;
font-weight: bold;
border-radius: 5px;
padding: 10px 20px;
transition: all 0.3s ease;
}
.stButton>button:hover {
background-color: #1e3799;
box-shadow: 0px 4px 8px rgba(0, 0, 0, 0.2);
}
.animated-div {
background: linear-gradient(-45deg, #4a69bd, #6a89cc, #54a0ff, #5f27cd);
background-size: 400% 400%;
animation: gradient 15s ease infinite;
padding: 15px;
border-radius: 10px;
margin-bottom: 15px;
color: white;
font-weight: bold;
}
@keyframes gradient {
0% {background-position: 0% 50%;}
50% {background-position: 100% 50%;}
100% {background-position: 0% 50%;}
}
.info-box {
background-color: #e3f2fd;
border-left: 5px solid #2196f3;
padding: 15px;
margin-bottom: 20px;
border-radius: 5px;
}
.warning-box {
background-color: #fff3e0;
border-left: 5px solid #ff9800;
padding: 15px;
margin-bottom: 20px;
border-radius: 5px;
}
</style>
""", unsafe_allow_html=True)
# Function to load Lottie animations
def load_lottieurl(url: str):
r = requests.get(url)
if r.status_code != 200:
return None
return r.json()
# Load Lottie animations
lottie_analytics = load_lottieurl("https://assets5.lottiefiles.com/packages/lf20_qp1q7mct.json")
lottie_skills = load_lottieurl("https://assets5.lottiefiles.com/private_files/lf30_wqypnpu5.json")
# Function to call the Together AI model
def call_ai_model(prompt):
url = "https://api.together.xyz/v1/chat/completions"
payload = {
"model": "NousResearch/Nous-Hermes-2-Yi-34B",
"temperature": 1.05,
"top_p": 0.9,
"top_k": 50,
"repetition_penalty": 1,
"n": 1,
"messages": [{"role": "user", "content": prompt}],
"stream_tokens": True,
}
TOGETHER_API_KEY = os.getenv('TOGETHER_API_KEY')
if TOGETHER_API_KEY is None:
raise ValueError("TOGETHER_API_KEY environment variable not set.")
headers = {
"accept": "application/json",
"content-type": "application/json",
"Authorization": f"Bearer {TOGETHER_API_KEY}",
}
try:
response = requests.post(url, json=payload, headers=headers, stream=True)
response.raise_for_status()
full_response = ""
for line in response.iter_lines():
if line:
line_content = line.decode('utf-8')
if line_content.startswith("data: "):
line_content = line_content[6:]
try:
json_data = json.loads(line_content)
if "choices" in json_data:
delta = json_data["choices"][0]["delta"]
if "content" in delta:
full_response += delta["content"]
except json.JSONDecodeError:
continue
return full_response.strip()
except requests.exceptions.RequestException as e:
st.error(f"API call failed: {e}")
return "An error occurred while fetching AI insights."
# Function to generate simulated data
def generate_simulated_data(categories, count=5):
return {cat: [random.randint(1, 100) for _ in range(count)] for cat in categories}
# Function to create word cloud
def create_word_cloud(text):
stopwords = set(stopwords.words('english'))
wordcloud = WordCloud(width=800, height=400, background_color='white', stopwords=stopwords).generate(text)
fig, ax = plt.subplots(figsize=(10, 5))
ax.imshow(wordcloud, interpolation='bilinear')
ax.axis('off')
return fig
# Function to create a downloadable Excel report
def create_excel_report(data):
output = io.BytesIO()
with pd.ExcelWriter(output, engine='xlsxwriter') as writer:
for sheet_name, df in data.items():
df.to_excel(writer, sheet_name=sheet_name, index=False)
output.seek(0)
return output
# Function for animated progress bar
def animated_progress_bar():
progress_bar = st.progress(0)
status_text = st.empty()
phrases = [
"Empowering youth with digital skills...",
"Unlocking your potential...",
"Building a brighter future...",
"Connecting skills to opportunities...",
"Transforming lives through technology..."
]
for i in range(100):
progress_bar.progress(i + 1)
status_text.text(random.choice(phrases))
time.sleep(0.05)
status_text.text("Ready to empower!")
time.sleep(0.5)
status_text.empty()
progress_bar.empty()
# Streamlit app layout
st.markdown('<div class="header">Digital Skills and Employment Analytics Platform (DSEAP)</div>', unsafe_allow_html=True)
st.markdown('<div class="subheader">Empowering Youth Through Data-Driven Insights</div>', unsafe_allow_html=True)
# Sidebar for navigation
st.sidebar.title("Navigation")
page = st.sidebar.radio("Go to", ["Home", "Skills Analysis", "Program Evaluation", "Barrier Identification", "Recommendations", "Reports"])
if page == "Home":
animated_progress_bar()
col1, col2 = st.columns([2, 1])
with col1:
st.markdown('<div class="section">', unsafe_allow_html=True)
st.subheader("Welcome to DSEAP")
st.write("""
The Digital Skills and Employment Analytics Platform (DSEAP) is an AI-driven tool designed to enhance
the evaluation and improvement of digital empowerment programs in Kenya. Our platform provides comprehensive
insights into the demand for digital skills, the effectiveness of existing programs, and the barriers faced
by youth in accessing digital opportunities. **A study In cmputing and prgramming**
""")
st.markdown('</div>', unsafe_allow_html=True)
with col2:
st_lottie(lottie_analytics, height=300, key="analytics")
st.markdown('<div class="section">', unsafe_allow_html=True)
st.subheader("Key Features")
col1, col2, col3 = st.columns(3)
with col1:
st.markdown("### ๐ Skills Demand Analysis")
st.write("Analyze job market trends and identify in-demand digital skills.")
with col2:
st.markdown("### ๐ Program Effectiveness")
st.write("Evaluate the impact of digital empowerment programs on youth employment.")
with col3:
st.markdown("### ๐ง Barrier Identification")
st.write("Identify and address obstacles to digital skill acquisition and employment.")
st.markdown('</div>', unsafe_allow_html=True)
st.markdown('<div class="info-box">', unsafe_allow_html=True)
st.info("๐ **Tip:** Use the sidebar to navigate through different sections of the platform.")
st.markdown('</div>', unsafe_allow_html=True)
elif page == "Skills Analysis":
animated_progress_bar()
st.markdown('<div class="section">', unsafe_allow_html=True)
st.subheader("Digital Skills Demand Analysis")
col1, col2 = st.columns([3, 1])
with col1:
skills = st.multiselect("Select skills to analyze:",
["Data Analysis", "Web Development", "Digital Marketing", "Cybersecurity", "Cloud Computing", "AI/Machine Learning", "Mobile App Development", "UI/UX Design"])
regions = st.multiselect("Select regions:",
["Kisii", "Nyamira", "Nairobi", "Mombasa", "Kisumu", "Nakuru", "Eldoret", "Thika", "Malindi", "Kitale"])
time_period = st.selectbox("Select time period:", ["Last 3 months", "Last 6 months", "Last year"])
with col2:
st_lottie(lottie_skills, height=200, key="skills")
if st.button("Analyze Skills Demand"):
with st.spinner("Analyzing skills demand..."):
# Simulated data generation (optimized)
demand_data = {skill: [random.randint(1, 100) for _ in regions] for skill in skills}
# Plotting (single chart instead of multiple)
df = pd.DataFrame(demand_data, index=regions)
fig = px.bar(df, barmode='group', title="Digital Skills Demand by Region")
st.plotly_chart(fig, use_container_width=True)
# AI Insights (with timeout)
ai_prompt = f"Briefly analyze the demand for {', '.join(skills)} in {', '.join(regions)} over the {time_period}. You can be negative on some skills too, just be free. Provide key insights on trends and gaps. Each region with its analysis and use percentages sometimes"
try:
with st.spinner("Generating AI insights..."):
ai_insights = call_ai_model(ai_prompt)
st.markdown("### AI Insights")
st.write(ai_insights)
except Exception as e:
st.error(f"Failed to generate AI insights: {str(e)}")
# Word Cloud (optional)
if st.checkbox("Generate Word Cloud"):
word_cloud_text = " ".join(skills + regions + ai_insights.split())
st.markdown("### Skills Demand Word Cloud")
st.pyplot(create_word_cloud(word_cloud_text))
# Skills growth projection (simplified)
st.markdown("### Skills Growth Projection")
growth_data = {skill: [random.uniform(0, 15) for _ in range(2)] for skill in skills}
years = [datetime.now().year, datetime.now().year + 5]
df_growth = pd.DataFrame(growth_data, index=years)
fig_growth = px.line(df_growth, title="Projected Skills Growth (5 Year Projection)")
st.plotly_chart(fig_growth, use_container_width=True)
st.markdown('</div>', unsafe_allow_html=True)
elif page == "Program Evaluation":
animated_progress_bar()
st.markdown('<div class="section">', unsafe_allow_html=True)
st.subheader("Digital Empowerment Program Evaluation")
programs = st.multiselect("Select programs to evaluate:",
["Ajira Digital", "Remotasks", "Upwork", "Fiverr", "Andela", "Emobiles", "snaphub", "iBizAfrica"])
metrics = st.multiselect("Select evaluation metrics:",
["Employment Rate", "Income Increase", "Skill Proficiency", "Program Completion Rate", "Job Satisfaction", "Career Advancement"])
if st.button("Evaluate Programs"):
with st.spinner("Evaluating programs..."):
# Simulated data generation
evaluation_data = generate_simulated_data(programs)
# Plotting
df = pd.DataFrame(evaluation_data)
fig = px.line(df, x=df.index, y=df.columns, labels={'index': 'Time (months)', 'value': 'Performance'}, title="Program Performance Over Time")
fig.update_layout(legend_title_text='Programs')
st.plotly_chart(fig)
# AI Insights
ai_prompt = f"Evaluate the effectiveness of {', '.join(programs)} based on {', '.join(metrics)}. Provide a detailed analysis of each program's performance, strengths, weaknesses, and recommendations for improvement."
ai_insights = call_ai_model(ai_prompt)
st.markdown("### AI-Powered Evaluation Insights")
st.write(ai_insights)
# Impact Visualization
impact_data = {prog: random.uniform(0, 1) for prog in programs}
fig = px.pie(values=list(impact_data.values()), names=list(impact_data.keys()), title="Program Impact Distribution")
st.plotly_chart(fig)
# Comparative Analysis
st.markdown("### Comparative Analysis")
comp_data = pd.DataFrame({metric: [random.uniform(0, 100) for _ in programs] for metric in metrics}, index=programs)
fig = px.imshow(comp_data, text_auto=True, aspect="auto", title="Program Performance Heatmap")
st.plotly_chart(fig)
st.markdown('</div>', unsafe_allow_html=True)
elif page == "Barrier Identification":
animated_progress_bar()
st.markdown('<div class="section">', unsafe_allow_html=True)
st.subheader("Digital Skills Acquisition Barriers")
barrier_categories = ["Access to Technology", "Digital Literacy", "Financial Constraints", "Cultural Factors", "Education System", "Gender Disparity", "Infrastructure", "Language Barriers"]
selected_barriers = st.multiselect("Select barrier categories to analyze:", barrier_categories)
if st.button("Identify Barriers"):
with st.spinner("Analyzing barriers..."):
# Simulated data generation
barrier_data = generate_simulated_data(selected_barriers)
# Plotting
fig = go.Figure(data=[go.Scatterpolar(
r=[max(barrier_data[cat]) for cat in selected_barriers],
theta=selected_barriers,
fill='toself'
)])
fig.update_layout(
polar=dict(
radialaxis=dict(visible=True),
),
showlegend=False
)
fig.update_layout(title="Barrier Intensity Analysis")
st.plotly_chart(fig)
# AI Insights
ai_prompt = f"with a precise answer, not so long and well structured for easy understanding. Analyze the barriers to digital skills acquisition in Kenya, focusing on {', '.join(selected_barriers)}. Provide a comprehensive breakdown of each barrier, its impact, and potential solutions."
ai_insights = call_ai_model(ai_prompt)
st.markdown("### AI-Generated Barrier Analysis")
st.write(ai_insights)
# Recommendations
st.markdown("### Recommended Interventions")
interventions = {
"Access to Technology": "Implement mobile learning programs and community technology centers",
"Digital Literacy": "Integrate digital skills into primary and secondary education curricula",
"Financial Constraints": "Provide scholarships and low-interest loans for digital skills training",
"Cultural Factors": "Develop culturally sensitive training materials and awareness campaigns",
"Education System": "Partner with local schools for early digital education integration",
"Gender Disparity": "Create women-focused digital skills programs and mentorship opportunities",
"Infrastructure": "Invest in rural broadband expansion and solar-powered internet kiosks",
"Language Barriers": "Develop multilingual digital learning resources and interfaces"
}
for barrier in selected_barriers:
st.write(f"- **{barrier}:** {interventions.get(barrier, 'Customized intervention based on specific barrier characteristics')}")
# Barrier Impact Over Time
st.markdown("### Projected Barrier Impact Over Time")
years = [datetime.now().year + i for i in range(5)]
impact_data = {barrier: [random.uniform(0, 100) for _ in range(5)] for barrier in selected_barriers}
fig = go.Figure()
for barrier, impact in impact_data.items():
fig.add_trace(go.Scatter(x=years, y=impact, mode='lines+markers', name=barrier))
fig.update_layout(title="Projected Barrier Impact (Next 5 Years)", xaxis_title="Year", yaxis_title="Impact Score")
st.plotly_chart(fig)
st.markdown('</div>', unsafe_allow_html=True)
elif page == "Recommendations":
animated_progress_bar()
st.markdown('<div class="section">', unsafe_allow_html=True)
st.subheader("Personalized Recommendations")
# User profile input
st.write("Enter your profile information to receive personalized recommendations:")
age = st.slider("Age", 15, 35, 25)
education = st.selectbox("Highest Education Level", ["High School", "Diploma", "Bachelor's", "Master's", "PhD"])
current_skills = st.multiselect("Current Digital Skills",
["Basic Computer Use", "Office Software", "Web Browsing", "Social Media",
"Programming", "Data Analysis", "Graphic Design", "Digital Marketing"])
career_goal = st.text_input("Career Goal")
location = st.selectbox("Current Location", ["Nairobi", "Mombasa", "Kisumu", "Nakuru", "Eldoret", "Kisii", "Nyamira"])
if st.button("Get Recommendations"):
with st.spinner("Generating personalized recommendations..."):
# AI-generated recommendations
ai_prompt = f"""Generate personalized digital skills recommendations for a {age}-year-old with {education} education,
skills in {', '.join(current_skills)}, located in {location}, aiming for a career in {career_goal}.
Provide a detailed learning path, skill priorities, job market insights, and potential challenges to overcome. Well structured for easy understanding"""
recommendations = call_ai_model(ai_prompt)
st.markdown("### Your Personalized Digital Skills Roadmap")
st.write(recommendations)
# Simulated skill gap analysis
st.markdown("### Skill Gap Analysis")
all_skills = ["Data Analysis", "Web Development", "Digital Marketing", "Cybersecurity", "Cloud Computing",
"AI/Machine Learning", "Mobile App Development", "UI/UX Design"]
skill_gaps = [skill for skill in all_skills if skill not in current_skills]
gap_scores = [random.randint(1, 100) for _ in skill_gaps]
fig = go.Figure(go.Bar(
x=skill_gaps,
y=gap_scores,
marker_color=['#1e3799', '#4a69bd', '#6a89cc', '#54a0ff', '#48dbfb']
))
fig.update_layout(title="Skill Gap Analysis", xaxis_title="Skills", yaxis_title="Proficiency Gap")
st.plotly_chart(fig)
# Job market alignment
st.markdown("### Job Market Alignment")
job_matches = ["Data Analyst", "Digital Marketing Specialist", "Front-end Developer", "UX Researcher", "Cybersecurity Analyst"]
match_scores = [random.uniform(0.5, 1) for _ in job_matches]
fig = px.bar(x=job_matches, y=match_scores, labels={'x': 'Job Titles', 'y': 'Match Score'}, title="Top Job Matches")
st.plotly_chart(fig)
# Learning resources
st.markdown("### Recommended Learning Resources")
resources = [
{"name": "Coursera", "type": "Online Courses", "link": "https://www.coursera.org/"},
{"name": "edX", "type": "Online Courses", "link": "https://www.edx.org/"},
{"name": "Udacity", "type": "Nanodegree Programs", "link": "https://www.udacity.com/"},
{"name": "FreeCodeCamp", "type": "Coding Tutorials", "link": "https://www.freecodecamp.org/"},
{"name": "Codecademy", "type": "Interactive Coding Lessons", "link": "https://www.codecademy.com/"}
]
for resource in resources:
st.write(f"- [{resource['name']}]({resource['link']}) - {resource['type']}")
st.markdown('</div>', unsafe_allow_html=True)
elif page == "Reports":
animated_progress_bar()
st.markdown('<div class="section">', unsafe_allow_html=True)
st.subheader("Generate Custom Reports")
report_type = st.selectbox("Select Report Type", ["Skills Demand", "Program Effectiveness", "Barrier Analysis"])
date_range = st.date_input("Select Date Range", [datetime.now() - timedelta(days=30), datetime.now()])
if st.button("Generate Report"):
with st.spinner("Generating report..."):
# Simulated report generation
if report_type == "Skills Demand":
skills = ["Data Analysis", "Web Development", "Digital Marketing", "Cybersecurity", "Cloud Computing"]
demand_data = pd.DataFrame({
"Skill": skills,
"Demand Score": [random.randint(50, 100) for _ in skills],
"Growth Rate": [random.uniform(0.5, 15) for _ in skills]
})
st.markdown("### Skills Demand Report")
st.dataframe(demand_data)
fig = px.scatter(demand_data, x="Demand Score", y="Growth Rate", size="Demand Score",
color="Skill", hover_name="Skill", size_max=60)
fig.update_layout(title="Skills Demand vs Growth Rate")
st.plotly_chart(fig)
elif report_type == "Program Effectiveness":
programs = ["Ajira Digital", "DigiTruck", "eMobilis", "KamiLimu", "Andela"]
metrics = ["Employment Rate", "Income Increase", "Skill Proficiency", "Program Completion Rate"]
effectiveness_data = pd.DataFrame({
metric: [random.uniform(60, 95) for _ in programs] for metric in metrics
}, index=programs)
st.markdown("### Program Effectiveness Report")
st.dataframe(effectiveness_data)
fig = px.imshow(effectiveness_data, text_auto=True, aspect="auto",
title="Program Effectiveness Heatmap")
st.plotly_chart(fig)
elif report_type == "Barrier Analysis":
barriers = ["Access to Technology", "Digital Literacy", "Financial Constraints", "Cultural Factors"]
impact_data = pd.DataFrame({
"Barrier": barriers,
"Impact Score": [random.uniform(3, 9) for _ in barriers],
"Affected Population (%)": [random.uniform(10, 50) for _ in barriers]
})
st.markdown("### Barrier Analysis Report")
st.dataframe(impact_data)
fig = px.bar(impact_data, x="Barrier", y="Impact Score", color="Affected Population (%)",
title="Barrier Impact Analysis")
st.plotly_chart(fig)
# Generate Excel report
excel_data = {
"Report": pd.DataFrame({"Report Type": [report_type], "Date Range": [f"{date_range[0]} to {date_range[1]}"]})
}
if report_type == "Skills Demand":
excel_data["Skills Demand"] = demand_data
elif report_type == "Program Effectiveness":
excel_data["Program Effectiveness"] = effectiveness_data
elif report_type == "Barrier Analysis":
excel_data["Barrier Analysis"] = impact_data
excel_report = create_excel_report(excel_data)
st.download_button(
label="Download Excel Report",
data=excel_report,
file_name=f"DSEAP_{report_type}_Report_{date_range[0]}_{date_range[1]}.xlsx",
mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
)
st.markdown('</div>', unsafe_allow_html=True)
# Footer
st.markdown('<div class="footer">ยฉ 2024 Digital Skills and Employment Analytics Platform (DSEAP) </div>', unsafe_allow_html=True)
# Add a warning about simulated data
st.markdown('<div class="warning-box">', unsafe_allow_html=True)
st.warning("**Note:** This is a prototype version Developed by Teresa Abuya, COD SIST")
st.markdown('</div>', unsafe_allow_html=True) |