Update app.py

app.py

@@ -4,7 +4,10 @@ import os
 import json
 import pandas as pd
 import plotly.graph_objects as go
+import plotly.express as px
 import time
+import random
+from datetime import datetime, timedelta
 
 # Custom CSS for styling
 st.markdown("""
@@ -67,10 +70,57 @@ def call_ai_model(all_message):
     }
 
     response = requests.post(url, json=payload, headers=headers, stream=True)
-    response.raise_for_status()  # Ensure HTTP request was successful
+    response.raise_for_status()
 
     return response
 
+# 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
+
+# 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 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 generate simulated geographic usage data
+def generate_geographic_data(technologies, regions):
+    geo_data = {}
+    for region in regions:
+        geo_data[region] = {}
+        for tech in technologies:
+            geo_data[region][tech] = random.uniform(0, 100)
+    return geo_data
+
+# 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
+
 # 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)
@@ -83,8 +133,8 @@ with st.container():
     issues = st.multiselect("Select issues of concern:", ["Trust", "Mistrust", "Data Privacy", "Fraud", "Social Classification"])
 
     st.subheader("Kenya-Specific Inputs")
-    region = st.text_input("Enter region in Kenya:")
-    gender_focus = st.multiselect("Select gender focus:", ["Male", "Female", "Non-binary", "All"])
+    regions = st.multiselect("Select regions in Kenya:", ["Nairobi", "Mombasa", "Kisumu", "Nakuru", "Eldoret"])
+    gender_focus = st.multiselect("Select gender focus:", ["Male", "Female", "Non-binary"])
     st.markdown('</div>', unsafe_allow_html=True)
 
 # Button to generate analysis
@@ -93,9 +143,9 @@ if st.button("Generate Analysis"):
         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"Region: {region}. Gender focus: {', '.join(gender_focus)}. "
+        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"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."
     )
@@ -114,7 +164,6 @@ if st.button("Generate Analysis"):
         ]
         
         with st.spinner("Analyzing digital technologies and generating analysis..."):
-            # Loop through each stage, updating the spinner text and waiting for 2 seconds
             for stage in stages:
                 st.spinner(stage)
                 time.sleep(2)
@@ -126,7 +175,7 @@ if st.button("Generate Analysis"):
                 if line:
                     line_content = line.decode('utf-8')
                     if line_content.startswith("data: "):
-                        line_content = line_content[6:]  # Strip "data: " prefix
+                        line_content = line_content[6:]
                     try:
                         json_data = json.loads(line_content)
                         if "choices" in json_data:
@@ -137,45 +186,107 @@ if st.button("Generate Analysis"):
                         continue
     
             st.success("Analysis completed!")
+            
             # Display analysis
             st.markdown('<div class="section">', unsafe_allow_html=True)
             st.subheader("Digital Technologies Impact Analysis in Kenya")
             st.markdown(analysis_text.strip())
             st.markdown('</div>', unsafe_allow_html=True)
 
-            # Prepare data for visualization
-            results_data = {
-                "Digital Technology": digital_technologies,
-                "Issues of Concern": issues
-            }
-            results_df = pd.DataFrame(results_data)
+            # 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)
+            geo_data = generate_geographic_data(digital_technologies, regions)
+            economic_impact = generate_economic_impact(digital_technologies)
 
-            # Display results in a table
+            # Trust and Fraud Metrics Visualization
             st.markdown('<div class="section">', unsafe_allow_html=True)
-            st.subheader("Digital Technologies Summary")
-            st.table(results_df)
+            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)
             st.markdown('</div>', unsafe_allow_html=True)
 
-            # Create a radar chart for digital technology impacts
+            # Kinship Structure Analysis
             st.markdown('<div class="section">', unsafe_allow_html=True)
-            fig = go.Figure(data=go.Scatterpolar(
-                r=[len(digital_technologies), len(issues)],
-                theta=["Digital Technologies", "Issues of Concern"],
-                fill='toself'
-            ))
-            fig.update_layout(
-                polar=dict(
-                    radialaxis=dict(visible=True, range=[0, max(len(digital_technologies), len(issues))])
-                ),
-                showlegend=False
+            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)
+            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)
+            st.markdown('</div>', unsafe_allow_html=True)
+
+            # Geographic Component
+            st.markdown('<div class="section">', unsafe_allow_html=True)
+            st.subheader("Geographic Distribution of Technology Usage")
+            fig_geo = px.choropleth(
+                locations=regions,
+                locationmode="country names",
+                color=[sum(geo_data[region].values()) for region in regions],
+                hover_name=regions,
+                color_continuous_scale=px.colors.sequential.Viridis,
+                title="Technology Usage Intensity Across Kenya"
             )
-            st.plotly_chart(fig)
+            fig_geo.update_geos(fitbounds="locations", visible=False)
+            st.plotly_chart(fig_geo)
+            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)
             st.markdown('</div>', unsafe_allow_html=True)
 
-            # Display raw analysis result for debugging
+            # Ethical Considerations
             st.markdown('<div class="section">', unsafe_allow_html=True)
-            with st.expander("Show Raw Analysis"):
-                st.text(analysis_text)
+            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)
 
     except ValueError as ve:
@@ -186,4 +297,4 @@ if st.button("Generate Analysis"):
         st.error(f"An unexpected error occurred: {e}")
 
 # Footer
-st.markdown('<div class="footer">Powered by Together AI and Streamlit</div>', unsafe_allow_html=True)
+st.markdown('<div class="footer">Powered by Together AI and Streamlit</div>', unsafe_allow_html=True)