Update app.py

app.py

@@ -8,61 +8,121 @@ 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="📊", 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: #f5f5f5;
+            background: #f0f2f6;
+            font-family: 'Roboto', sans-serif;
         }
         .header {
-            font-size: 36px;
+            font-size: 42px;
             font-weight: bold;
-            color: #4CAF50;
+            color: #1e3799;
             text-align: center;
-            margin-bottom: 20px;
+            margin-bottom: 30px;
+            text-shadow: 2px 2px 4px rgba(0,0,0,0.1);
         }
         .subheader {
-            font-size: 24px;
+            font-size: 28px;
             font-weight: bold;
-            color: #4CAF50;
+            color: #3c6382;
             text-align: center;
-            margin-bottom: 20px;
+            margin-bottom: 25px;
         }
         .section {
             background: white;
-            padding: 20px;
-            border-radius: 10px;
-            box-shadow: 0px 4px 12px rgba(0, 0, 0, 0.1);
-            margin-bottom: 20px;
+            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: #777;
-            margin-top: 20px;
+            color: #555;
+            margin-top: 30px;
+            padding: 10px;
+            border-top: 1px solid #ddd;
         }
         .stProgress > div > div > div > div {
-            background-image: linear-gradient(to right, #4CAF50, #45a049);
+            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%;}
         }
-        .animated-div {
-            background: linear-gradient(-45deg, #ee7752, #e73c7e, #23a6d5, #23d5ab);
-            background-size: 400% 400%;
-            animation: gradient 15s ease infinite;
-            padding: 10px;
+        .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;
-            margin-bottom: 10px;
         }
     </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(all_message):
+def call_ai_model(prompt):
     url = "https://api.together.xyz/v1/chat/completions"
     payload = {
         "model": "NousResearch/Nous-Hermes-2-Yi-34B",
@@ -71,7 +131,7 @@ def call_ai_model(all_message):
         "top_k": 50,
         "repetition_penalty": 1,
         "n": 1,
-        "messages": [{"role": "user", "content": all_message}],
+        "messages": [{"role": "user", "content": prompt}],
         "stream_tokens": True,
     }
 
@@ -85,228 +145,378 @@ def call_ai_model(all_message):
         "Authorization": f"Bearer {TOGETHER_API_KEY}",
     }
 
-    response = requests.post(url, json=payload, headers=headers, stream=True)
-    response.raise_for_status()
-
-    return response
-
-# Function to process AI response
-def process_ai_response(response):
-    explanation_text = ""
-    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:
-                        explanation_text += delta["content"]
-            except json.JSONDecodeError:
-                continue
-    return explanation_text.strip()
-
-# Function to get AI explanation for graphs
-def get_ai_explanation(graph_type, data):
-    explanation_prompt = f"Provide a short, clear explanation of the following {graph_type} graph data: {data}"
-    response = call_ai_model(explanation_prompt)
-    explanation = process_ai_response(response)
-    return explanation
-
-# Function to generate simulated trust scores
-def generate_trust_scores(technologies, issues):
-    trust_scores = {}
-    for tech in technologies:
-        trust_scores[tech] = {}
-        for issue in issues:
-            trust_scores[tech][issue] = random.uniform(0, 1)
-    return trust_scores
+    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 kinship impact scores
-def generate_kinship_impact(technologies):
-    impact_scores = {}
-    kinship_aspects = ["Family Communication", "Intergenerational Relationships", "Cultural Traditions"]
-    for tech in technologies:
-        impact_scores[tech] = {}
-        for aspect in kinship_aspects:
-            impact_scores[tech][aspect] = random.uniform(-1, 1)
-    return impact_scores
+# 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 generate simulated gender impact scores
-def generate_gender_impact(technologies, genders):
-    impact_scores = {}
-    for tech in technologies:
-        impact_scores[tech] = {}
-        for gender in genders:
-            impact_scores[tech][gender] = random.uniform(-1, 1)
-    return impact_scores
+# 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 generate simulated long-term economic impact
-def generate_economic_impact(technologies):
-    impact_data = {}
-    indicators = ["GDP Growth", "Employment Rate", "Digital Literacy"]
-    for tech in technologies:
-        impact_data[tech] = {}
-        for indicator in indicators:
-            impact_data[tech][indicator] = [random.uniform(-2, 5) for _ in range(5)]  # 5-year projection
-    return impact_data
+# 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
 
 # Streamlit app layout
-st.markdown('<div class="header">Digital Technologies, Kinship, and Gender in Kenya</div>', unsafe_allow_html=True)
-st.markdown('<div class="subheader">Analyze and visualize the impact of digital technologies on kinship and gender dynamics in Kenya.</div>', unsafe_allow_html=True)
-
-# Input section
-with st.container():
-    st.markdown('<div class="section">', unsafe_allow_html=True)
-    st.subheader("Digital Technology Impacts")
-    digital_technologies = st.multiselect("Select digital technologies:", ["Big Data Analytics", "Biometric Authentication", "Blockchain", "E-commerce", "Social Media Platforms"])
-    issues = st.multiselect("Select issues of concern:", ["Trust", "Mistrust", "Data Privacy", "Fraud", "Social Classification"])
-
-    st.subheader("Kenya-Specific Inputs")
-    regions = st.multiselect("Select regions in Kenya:", ["Nairobi", "Coast", "Nyanza", "Rift valley", "Eastern", "North Eastern"])
-    gender_focus = st.multiselect("Select gender focus:", ["Male", "Female", "Non-binary"])
-    st.markdown('</div>', unsafe_allow_html=True)
+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)
 
-# Button to generate analysis
-if st.button("Generate Analysis"):
-    all_message = (
-        f"Analyze the impact of digital technologies on kinship and gender dynamics in Kenya. "
-        f"Digital technologies: {', '.join(digital_technologies)}. "
-        f"Issues of concern: {', '.join(issues)}. "
-        f"Regions: {', '.join(regions)}. Gender focus: {', '.join(gender_focus)}. "
-        f"Provide a detailed analysis of how these technologies impact family ties, trust, and gender roles. "
-        f"Include specific impacts for each digital technology and issue. "
-        f"Organize the information in tables with the following columns: Digital Technology, Impact on Kinship, Impact on Gender Dynamics, Trust Issues. "
-        f"Be as accurate and specific to Kenya as possible in your analysis. Make the response short and precise. Do not give anything like a conclusion after generating"
-    )
-
-    try:
-        stages = [
-            "Analyzing digital technologies...",
-            "Running simulations...",
-            "Processing data...",
-            "Assessing impacts...",
-            "Calculating predictions...",
-            "Compiling results...",
-            "Finalizing analysis...",
-            "Preparing output..."
-        ]
-        
-        progress_bar = st.progress(0)
-        status_text = st.empty()
-
-        for i, stage in enumerate(stages):
-            status_text.markdown(f'<div class="animated-div">{stage}</div>', unsafe_allow_html=True)
-            progress_bar.progress((i + 1) / len(stages))
-            time.sleep(1)
-        
-        response = call_ai_model(all_message)
-        analysis_text = process_ai_response(response)
+# Sidebar for navigation
+st.sidebar.title("Navigation")
+page = st.sidebar.radio("Go to", ["Home", "Skills Analysis", "Program Evaluation", "Barrier Identification", "Recommendations", "Reports"])
 
-        st.success("Analysis completed!")
-        
-        # Display analysis
+if page == "Home":
+    col1, col2 = st.columns([2, 1])
+    with col1:
         st.markdown('<div class="section">', unsafe_allow_html=True)
-        st.subheader("Digital Technologies Impact Analysis in Kenya")
-        st.markdown(analysis_text)
+        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.
+        """)
         st.markdown('</div>', unsafe_allow_html=True)
+    with col2:
+        st_lottie(lottie_analytics, height=300, key="analytics")
 
-        # Generate simulated data
-        trust_scores = generate_trust_scores(digital_technologies, issues)
-        kinship_impact = generate_kinship_impact(digital_technologies)
-        gender_impact = generate_gender_impact(digital_technologies, gender_focus)
-        economic_impact = generate_economic_impact(digital_technologies)
+    st.markdown('<div class="section">', unsafe_allow_html=True)
+    st.subheader("Key Features")
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        st.markdown("### 📊 Skills Demand Analysis")
+        st.write("Analyze job market trends and identify in-demand digital skills.")
+    with col2:
+        st.markdown("### 📈 Program Effectiveness")
+        st.write("Evaluate the impact of digital empowerment programs on youth employment.")
+    with col3:
+        st.markdown("### 🚧 Barrier Identification")
+        st.write("Identify and address obstacles to digital skill acquisition and employment.")
+    st.markdown('</div>', unsafe_allow_html=True)
 
-        # Trust and Fraud Metrics Visualization
-        st.markdown('<div class="section">', unsafe_allow_html=True)
-        st.subheader("Trust and Fraud Metrics")
-        fig_trust = go.Figure()
-        for tech in digital_technologies:
-            fig_trust.add_trace(go.Bar(
-                x=list(trust_scores[tech].keys()),
-                y=list(trust_scores[tech].values()),
-                name=tech
-            ))
-        fig_trust.update_layout(barmode='group', title="Trust Scores by Technology and Issue")
-        st.plotly_chart(fig_trust)
-        trust_explanation = get_ai_explanation("Trust and Fraud Metrics", trust_scores)
-        st.markdown(f"**AI Explanation:** {trust_explanation}")
-        st.markdown('</div>', unsafe_allow_html=True)
+    st.markdown('<div class="info-box">', unsafe_allow_html=True)
+    st.info("📌 **Tip:** Use the sidebar to navigate through different sections of the platform.")
+    st.markdown('</div>', unsafe_allow_html=True)
 
-        # Kinship Structure Analysis
-        st.markdown('<div class="section">', unsafe_allow_html=True)
-        st.subheader("Impact on Kinship Structures")
-        fig_kinship = go.Figure()
-        for tech in digital_technologies:
-            fig_kinship.add_trace(go.Scatterpolar(
-                r=list(kinship_impact[tech].values()),
-                theta=list(kinship_impact[tech].keys()),
-                fill='toself',
-                name=tech
-            ))
-        fig_kinship.update_layout(polar=dict(radialaxis=dict(visible=True, range=[-1, 1])), showlegend=True)
-        st.plotly_chart(fig_kinship)
-        kinship_explanation = get_ai_explanation("Impact on Kinship Structures", kinship_impact)
-        st.markdown(f"**AI Explanation:** {kinship_explanation}")
-        st.markdown('</div>', unsafe_allow_html=True)
+elif page == "Skills Analysis":
+    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:", 
+                                 ["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
+            demand_data = generate_simulated_data(skills)
+            
+            # Plotting
+            fig = go.Figure()
+            for skill in skills:
+                fig.add_trace(go.Bar(x=regions, y=demand_data[skill], name=skill))
+            fig.update_layout(title="Digital Skills Demand by Region", barmode='group')
+            st.plotly_chart(fig)
+            
+            # AI Insights
+            ai_prompt = f"Analyze the demand for {', '.join(skills)} in {', '.join(regions)} over the {time_period}. Provide insights on trends, gaps, and recommendations for skill development."
+            ai_insights = call_ai_model(ai_prompt)
+            st.markdown("### AI Insights")
+            st.write(ai_insights)
+            
+            # 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
+            st.markdown("### Skills Growth Projection")
+            growth_data = {skill: [random.uniform(0, 15) for _ in range(5)] for skill in skills}
+            years = [datetime.now().year + i for i in range(5)]
+            fig = go.Figure()
+            for skill, growth in growth_data.items():
+                fig.add_trace(go.Scatter(x=years, y=growth, mode='lines+markers', name=skill))
+            fig.update_layout(title="Projected Skills Growth (Next 5 Years)", xaxis_title="Year", yaxis_title="Growth Rate (%)")
+            st.plotly_chart(fig)
+    
+    st.markdown('</div>', unsafe_allow_html=True)
 
-        # Gender Impact Visualization
-        st.markdown('<div class="section">', unsafe_allow_html=True)
-        st.subheader("Gender Impact Analysis")
-        fig_gender = go.Figure()
-        for tech in digital_technologies:
-            fig_gender.add_trace(go.Bar(
-                x=list(gender_impact[tech].keys()),
-                y=list(gender_impact[tech].values()),
-                name=tech
-            ))
-        fig_gender.update_layout(barmode='group', title="Gender Impact by Technology")
-        st.plotly_chart(fig_gender)
-        gender_explanation = get_ai_explanation("Gender Impact Analysis", gender_impact)
-        st.markdown(f"**AI Explanation:** {gender_explanation}")
-        st.markdown('</div>', unsafe_allow_html=True)
+elif page == "Program Evaluation":
+    st.markdown('<div class="section">', unsafe_allow_html=True)
+    st.subheader("Digital Empowerment Program Evaluation")
+    
+    programs = st.multiselect("Select programs to evaluate:", 
+                              ["Ajira Digital", "DigiTruck", "eMobilis", "KamiLimu", "Andela", "iHub", "NaiLab", "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
+            fig = px.line(x=range(len(next(iter(evaluation_data.values())))), y=evaluation_data.values(), 
+                          labels={'x': 'Time (months)', 'y': '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=impact_data.values(), names=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)
 
-        # Long-term Economic Impact
-        st.markdown('<div class="section">', unsafe_allow_html=True)
-        st.subheader("Projected Long-term Economic Impact")
-        fig_economic = go.Figure()
-        years = [datetime.now().year + i for i in range(5)]
-        for tech in digital_technologies:
-            for indicator in economic_impact[tech]:
-                fig_economic.add_trace(go.Scatter(
-                    x=years,
-                    y=economic_impact[tech][indicator],
-                    mode='lines+markers',
-                    name=f"{tech} - {indicator}"
-                ))
-        fig_economic.update_layout(title="5-Year Economic Impact Projection", xaxis_title="Year", yaxis_title="Impact (%)")
-        st.plotly_chart(fig_economic)
-        economic_explanation = get_ai_explanation("Projected Long-term Economic Impact", economic_impact)
-        st.markdown(f"**AI Explanation:** {economic_explanation}")
-        st.markdown('</div>', unsafe_allow_html=True)
+elif page == "Barrier Identification":
+    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.Radar(
+                r=[max(barrier_data[cat]) for cat in selected_barriers],
+                theta=fig = go.Figure(data=[go.Radar(
+                r=[max(barrier_data[cat]) for cat in selected_barriers],
+                theta=selected_barriers,
+                fill='toself'
+            )])
+            fig.update_layout(title="Barrier Intensity Analysis")
+            st.plotly_chart(fig)
+            
+            # AI Insights
+            ai_prompt = f"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)
 
-        # Ethical Considerations
-        st.markdown('<div class="section">', unsafe_allow_html=True)
-        st.subheader("Ethical Considerations")
-        ethical_concerns = [
-            "Data Privacy: Ensuring user data is protected and used responsibly.",
-            "Digital Divide: Addressing inequality in access to digital technologies.",
-            "Cultural Preservation: Balancing technological advancement with traditional values.",
-            "Algorithmic Bias: Mitigating biases in AI and machine learning systems.",
-            "Cybersecurity: Protecting users from fraud and cyber attacks"
-        ]
-        for concern in ethical_concerns:
-            st.write(f"• {concern}")
-        st.markdown('</div>', unsafe_allow_html=True)
+elif page == "Recommendations":
+    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", "Other"])
+    
+    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."""
+            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)
 
-    except ValueError as ve:
-        st.error(f"Configuration error: {ve}")
-    except requests.exceptions.RequestException as re:
-        st.error(f"Request error: {re}")
-    except Exception as e:
-        st.error(f"An unexpected error occurred: {e}")
+elif page == "Reports":
+    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">Developed by TERESA ABUYA</div>', unsafe_allow_html=True)
+st.markdown('<div class="footer">© 2024 Digital Skills and Employment Analytics Platform (DSEAP) | Developed by Your Team</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)