Update app.py

app.py

@@ -6,8 +6,8 @@ import pandas as pd
 import plotly.graph_objects as go
 import plotly.express as px
 import time
-import random
 from datetime import datetime, timedelta
+import random
 
 # Custom CSS for styling
 st.markdown("""
@@ -42,6 +42,22 @@ st.markdown("""
             color: #777;
             margin-top: 20px;
         }
+        .stProgress > div > div > div > div {
+            background-image: linear-gradient(to right, #4CAF50, #45a049);
+        }
+        @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;
+            border-radius: 5px;
+            margin-bottom: 10px;
+        }
     </style>
 """, unsafe_allow_html=True)
 
@@ -74,6 +90,31 @@ def call_ai_model(all_message):
 
     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 = {}
@@ -102,15 +143,6 @@ def generate_gender_impact(technologies, 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 = {}
@@ -153,7 +185,6 @@ if st.button("Generate Analysis"):
     try:
         stages = [
             "Analyzing digital technologies...",
-            "Checking location data...",
             "Running simulations...",
             "Processing data...",
             "Assessing impacts...",
@@ -163,131 +194,112 @@ if st.button("Generate Analysis"):
             "Preparing output..."
         ]
         
-        with st.spinner("Analyzing digital technologies and generating analysis..."):
-            for stage in stages:
-                st.spinner(stage)
-                time.sleep(2)
-            
-            response = call_ai_model(all_message)
-    
-            analysis_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:
-                                analysis_text += delta["content"]
-                    except json.JSONDecodeError:
-                        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)
+        progress_bar = st.progress(0)
+        status_text = st.empty()
 
-            # 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)
+        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)
 
-            # 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)
-            st.markdown('</div>', unsafe_allow_html=True)
+        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)
+        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)
-            st.markdown('</div>', unsafe_allow_html=True)
+        # 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)
 
-            # 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)
+        # 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)
+
+        # 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)
 
-            # 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"
-            )
-            fig_geo.update_geos(fitbounds="locations", visible=False)
-            st.plotly_chart(fig_geo)
-            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)
 
-            # 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)
+        # 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)
 
-            # 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)
+        # 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)
 
     except ValueError as ve:
         st.error(f"Configuration error: {ve}")