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| 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 | |
| # Custom CSS for styling | |
| st.markdown(""" | |
| <style> | |
| .stApp { | |
| background: #f5f5f5; | |
| } | |
| .header { | |
| font-size: 36px; | |
| font-weight: bold; | |
| color: #4CAF50; | |
| text-align: center; | |
| margin-bottom: 20px; | |
| } | |
| .subheader { | |
| font-size: 24px; | |
| font-weight: bold; | |
| color: #4CAF50; | |
| text-align: center; | |
| margin-bottom: 20px; | |
| } | |
| .section { | |
| background: white; | |
| padding: 20px; | |
| border-radius: 10px; | |
| box-shadow: 0px 4px 12px rgba(0, 0, 0, 0.1); | |
| margin-bottom: 20px; | |
| } | |
| .footer { | |
| text-align: center; | |
| font-size: 14px; | |
| 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) | |
| # Function to call the Together AI model | |
| def call_ai_model(all_message): | |
| 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": all_message}], | |
| "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}", | |
| } | |
| 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 | |
| # 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 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) | |
| # 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", "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 | |
| 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) | |
| 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) | |
| # 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) | |
| # 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) | |
| # 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) | |
| 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) | |
| 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}") | |
| # Footer | |
| st.markdown('<div class="footer">Developed by TERESA ABUYA</div>', unsafe_allow_html=True) |