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# import gradio as gr
# import os
# from mistralai.client import MistralClient
# from mistralai.models.chat_completion import ChatMessage

# # Ensure the environment variable for the API key is set
# api_key = os.getenv("MISTRAL_API_KEY")
# if not api_key:
#     raise ValueError("MISTRAL_API_KEY environment variable not set")

# model = "mistral-tiny"
# client = MistralClient(api_key=api_key)

# def generate_goals(input_var):
#     messages = [
#         ChatMessage(role="user", content=f"Generate 10 specific, industry relevant goals for {input_var} using Python and Pandas. Each goal should include a brief name and a one-sentence description of the task or skill. Focus on practical applications in educational assessment, covering areas such as data processing, statistical analysis, visualization, and advanced techniques")
#     ]

#     try:
#         response = client.chat(model=model, messages=messages)
#         content = response.choices[0].message.content
#         return content
#     except Exception as e:
#         return f"An error occurred: {str(e)}"

# # HTML content
# html_content = """
# <!DOCTYPE html>
# <html lang="en">
# <head>
#     <meta charset="UTF-8">
#     <meta name="viewport" content="width=device-width, initial-scale=1.0">
#     <title>Comprehensive Exam Data Analysis with Pandas - 30 Industry Goals with Connections</title>
#     <script src="https://cdnjs.cloudflare.com/ajax/libs/d3/7.8.5/d3.min.js"></script>
#     <style>
#         body { font-family: Arial, sans-serif; margin: 20px; }
#         #goalSpace { border: 1px solid #ccc; margin-bottom: 20px; }
#         .goal { cursor: pointer; }
#         #info { margin-top: 20px; font-weight: bold; }
#         #selectedGoal { margin-top: 10px; padding: 10px; border: 1px solid #ccc; background-color: #f0f0f0; }
#         #hoverInfo { 
#             position: absolute; 
#             padding: 10px; 
#             background-color: rgba(255, 255, 255, 0.9); 
#             border: 1px solid #ccc; 
#             border-radius: 5px; 
#             font-size: 14px;
#             max-width: 300px;
#             display: none;
#         }
#         #responseBox {
#             margin-top: 20px;
#             padding: 10px;
#             border: 1px solid #ccc;
#             background-color: #e0f7fa;
#         }
#     </style>
# </head>
# <body>
#     <h1>Comprehensive Exam Data Analysis with Pandas - 30 Industry Goals with Connections</h1>
#     <div id="goalSpace"></div>
#     <div id="info"></div>
#     <div id="selectedGoal"></div>
#     <div id="hoverInfo"></div>
#     <div id="responseBox"></div>

#     <script>
#         const width = 1200;
#         const height = 800;
#         // Define the goals and connections data
#         const goals = [
#             { id: 1, x: 100, y: 400, name: "Automate Data Import", description: "Develop scripts to automate exam data extraction from various sources (CSV, Excel, databases) using Pandas read_* functions." },
#             { id: 2, x: 200, y: 300, name: "Data Cleaning", description: "Implement robust data cleaning processes to handle missing values, outliers, and inconsistencies in exam data using Pandas methods like dropna(), fillna(), and apply()." },
#             { id: 3, x: 300, y: 200, name: "Data Transformation", description: "Utilize Pandas for complex data transformations such as pivoting exam results, melting question-wise scores, and creating derived features for analysis." },
#             { id: 4, x: 400, y: 300, name: "Statistical Analysis", description: "Develop functions to automate statistical analysis of exam results, including descriptive statistics, hypothesis testing, and correlation analysis using Pandas and SciPy." },
#             { id: 5, x: 500, y: 400, name: "Performance Metrics", description: "Create custom functions to calculate industry-standard exam performance metrics like item difficulty, discrimination index, and reliability coefficients using Pandas operations." },
#             { id: 6, x: 200, y: 500, name: "Data Filtering", description: "Implement advanced filtering techniques to segment exam data based on various criteria (e.g., demographic info, score ranges) using boolean indexing and query() method in Pandas." },
#             { id: 7, x: 300, y: 600, name: "Reporting Automation", description: "Develop automated reporting systems that use Pandas groupby() and agg() functions to generate summary statistics and performance reports for different exam cohorts." },
#             { id: 8, x: 400, y: 500, name: "Data Visualization", description: "Create interactive dashboards for exam data visualization using Pandas with Plotly or Bokeh, allowing stakeholders to explore results dynamically." },
#             { id: 9, x: 500, y: 600, name: "Time Series Analysis", description: "Implement time series analysis techniques using Pandas datetime functionality to track and forecast exam performance trends over multiple test administrations." },
#             { id: 10, x: 300, y: 400, name: "Data Integration", description: "Develop processes to merge exam data with other relevant datasets (e.g., student information systems, learning management systems) using Pandas merge() and join() operations." },
#             { id: 11, x: 600, y: 300, name: "Performance Optimization", description: "Improve the efficiency of Pandas operations on large exam datasets by utilizing techniques like chunking, multiprocessing, and query optimization." },
#             { id: 12, x: 700, y: 400, name: "Machine Learning Integration", description: "Integrate machine learning models with Pandas for predictive analytics, such as predicting exam success or identifying at-risk students based on historical data." },
#             { id: 13, x: 800, y: 500, name: "Custom Indexing", description: "Implement custom indexing strategies in Pandas to efficiently handle hierarchical exam data structures and improve data access patterns." },
#             { id: 14, x: 900, y: 400, name: "Data Anonymization", description: "Develop Pandas-based workflows to anonymize sensitive exam data, ensuring compliance with privacy regulations while maintaining data utility for analysis." },
#             { id: 15, x: 1000, y: 300, name: "Exam Item Analysis", description: "Create specialized functions using Pandas to perform detailed item analysis, including distractor analysis and reliability calculations for individual exam questions." },
#             { id: 16, x: 600, y: 500, name: "Longitudinal Analysis", description: "Implement Pandas-based methods for tracking student performance across multiple exams over time, identifying learning trends and progress patterns." },
#             { id: 17, x: 700, y: 600, name: "Adaptive Testing Analysis", description: "Develop analysis pipelines using Pandas to evaluate and optimize adaptive testing algorithms, including item selection strategies and scoring methods." },
#             { id: 18, x: 800, y: 700, name: "Exam Equating", description: "Create Pandas workflows to perform exam equating, ensuring comparability of scores across different versions or administrations of an exam." },
#             { id: 19, x: 900, y: 600, name: "Response Time Analysis", description: "Utilize Pandas to analyze exam response times, identifying patterns that may indicate guessing, test-taking strategies, or item difficulty." },
#             { id: 20, x: 1000, y: 500, name: "Collaborative Filtering", description: "Implement collaborative filtering techniques using Pandas to recommend study materials or practice questions based on exam performance patterns." },
#             { id: 21, x: 400, y: 700, name: "Exam Fraud Detection", description: "Develop anomaly detection algorithms using Pandas to identify potential exam fraud or unusual response patterns in large-scale testing programs." },
#             { id: 22, x: 500, y: 800, name: "Standard Setting", description: "Create Pandas-based tools to assist in standard setting processes, analyzing expert judgments and examinee data to establish performance standards." },
#             { id: 23, x: 600, y: 700, name: "Automated Reporting", description: "Implement automated report generation using Pandas and libraries like Jinja2 to create customized, data-driven exam reports for various stakeholders." },
#             { id: 24, x: 700, y: 800, name: "Cross-validation", description: "Develop cross-validation frameworks using Pandas to assess the reliability and generalizability of predictive models in educational assessment contexts." },
#             { id: 25, x: 800, y: 300, name: "API Integration", description: "Create Pandas-based interfaces to integrate exam data analysis workflows with external APIs, facilitating real-time data exchange and reporting." },
#             { id: 26, x: 900, y: 200, name: "Natural Language Processing", description: "Implement NLP techniques using Pandas and libraries like NLTK to analyze free-text responses in exams, enabling automated scoring and content analysis." },
#             { id: 27, x: 1000, y: 100, name: "Exam Blueprint Analysis", description: "Develop Pandas workflows to analyze exam blueprints, ensuring content coverage and alignment with learning objectives across multiple test forms." },
#             { id: 28, x: 100, y: 600, name: "Differential Item Functioning", description: "Implement statistical methods using Pandas to detect and analyze differential item functioning (DIF) in exams, ensuring fairness across different demographic groups." },
#             { id: 29, x: 200, y: 700, name: "Automated Feedback Generation", description: "Create Pandas-based systems to generate personalized feedback for test-takers based on their exam performance and identified areas for improvement." },
#             { id: 30, x: 300, y: 800, name: "Exam Security Analysis", description: "Develop analytical tools using Pandas to assess and enhance exam security, including analysis of item exposure rates and detection of potential security breaches." }
#         ];
#         const connections = [
#             { source: 1, target: 2 },
#             { source: 2, target: 3 },
#             { source: 3, target: 4 },
#             { source: 4, target: 5 },
#             { source: 5, target: 7 },
#             { source: 6, target: 7 },
#             { source: 7, target: 8 },
#             { source: 8, target: 9 },
#             { source: 9, target: 16 },
#             { source: 10, target: 13 },
#             { source: 11, target: 12 },
#             { source: 12, target: 20 },
#             { source: 13, target: 16 },
#             { source: 14, target: 21 },
#             { source: 15, target: 17 },
#             { source: 16, target: 18 },
#             { source: 17, target: 19 },
#             { source: 18, target: 22 },
#             { source: 19, target: 21 },
#             { source: 20, target: 29 },
#             { source: 21, target: 30 },
#             { source: 22, target: 23 },
#             { source: 23, target: 25 },
#             { source: 24, target: 12 },
#             { source: 25, target: 23 },
#             { source: 26, target: 15 },
#             { source: 27, target: 15 },
#             { source: 28, target: 22 },
#             { source: 29, target: 23 },
#             { source: 30, target: 21 },
#             // Additional connections for more interconnectivity
#             { source: 1, target: 10 },
#             { source: 2, target: 6 },
#             { source: 3, target: 13 },
#             { source: 4, target: 15 },
#             { source: 5, target: 28 },
#             { source: 8, target: 23 },
#             { source: 11, target: 25 },
#             { source: 14, target: 30 },
#             { source: 24, target: 17 },
#             { source: 26, target: 29 }
#         ];

        
#         // Create the SVG container for the goals and connections
#         const svg = d3.select("#goalSpace")
#             .append("svg")
#             .attr("width", width)
#             .attr("height", height);
        
#         // Draw connections between goals
#         const links = svg.selectAll("line")
#             .data(connections)
#             .enter()
#             .append("line")
#             .attr("x1", d => goals.find(g => g.id === d.source).x)
#             .attr("y1", d => goals.find(g => g.id === d.source).y)
#             .attr("x2", d => goals.find(g => g.id === d.target).x)
#             .attr("y2", d => goals.find(g => g.id === d.target).y)
#             .attr("stroke", "#999")
#             .attr("stroke-width", 1)
#             .attr("stroke-opacity", 0.6);
        
#         // Draw goal nodes
#         const goalNodes = svg.selectAll("circle")
#             .data(goals)
#             .enter()
#             .append("circle")
#             .attr("cx", d => d.x)
#             .attr("cy", d => d.y)
#             .attr("r", 10)
#             .attr("fill", d => {
#                 if (d.id <= 10) return "blue";
#                 if (d.id <= 20) return "green";
#                 return "orange";
#             })
#             .attr("class", "goal");
        
#         // Add labels to the goals
#         const goalLabels = svg.selectAll("text")
#             .data(goals)
#             .enter()
#             .append("text")
#             .attr("x", d => d.x + 15)
#             .attr("y", d => d.y)
#             .text(d => d.name)
#             .attr("font-size", "12px");
        
#         // Hover info box
#         const hoverInfo = d3.select("#hoverInfo");
        
#         // Add hover effects on goal nodes
#         goalNodes.on("mouseover", function(event, d) {
#             d3.select(this).attr("r", 15);
#             hoverInfo.style("display", "block")
#                 .style("left", (event.pageX + 10) + "px")
#                 .style("top", (event.pageY - 10) + "px")
#                 .html(`<strong>${d.name}</strong><br>${d.description}`);
#         }).on("mouseout", function() {
#             d3.select(this).attr("r", 10);
#             hoverInfo.style("display", "none");
#         });
        
#         // Handle click event on goal nodes
#         goalNodes.on("click", async function(event, d) {
#             updateSelectedGoalInfo(d);
            
#             try {
#                 const response = await fetch('generate_goals', {
#                     method: 'POST',
#                     headers: {
#                         'Content-Type': 'application/json',
#                     },
#                     body: JSON.stringify({ input_var: d.name })
#                 });
                
#                 if (!response.ok) {
#                     throw new Error(`HTTP error! status: ${response.status}`);
#                 }
                
#                 const data = await response.json();
#                 displayResponse(data.content);
#             } catch (error) {
#                 console.error("There was an error fetching the response:", error);
#                 displayResponse("An error occurred while generating the response.");
#             }
#         });
        
#         // Function to update selected goal information
#         function updateSelectedGoalInfo(goal) {
#             const selectedGoalDiv = d3.select("#selectedGoal");
#             selectedGoalDiv.html(`
#                 <h3>${goal.name}</h3>
#                 <p>${goal.description}</p>
#             `);
#         }
        
#         // Function to display the response from the server
#         function displayResponse(content) {
#             const responseBox = d3.select("#responseBox");
#             responseBox.html(`
#                 <h2>Response</h2>
#                 <p>${content}</p>
#             `);
#         }
        
#         // Handle mouse move event to highlight the closest goal
#         svg.on("mousemove", function(event) {
#             const [x, y] = d3.pointer(event);
#             const closest = findClosestGoal(x, y);
#             highlightClosestGoal(closest);
#         });
        
#         // Function to find the closest goal to the mouse pointer
#         function findClosestGoal(x, y) {
#             return goals.reduce((closest, goal) => {
#                 const distance = Math.sqrt(Math.pow(goal.x - x, 2) + Math.pow(goal.y - y, 2));
#                 return distance < closest.distance ? { goal, distance } : closest;
#             }, { goal: null, distance: Infinity }).goal;
#         }
        
#         // Function to highlight the closest goal
#         function highlightClosestGoal(goal) {
#             d3.select("#info").html(`Closest goal: ${goal.name}`);
#         }
#     </script>
# </body>
# </html>
# """

# # Gradio interface
# iface = gr.Interface(
#     fn=generate_goals,
#     inputs=gr.Textbox(label="Goal Name"),
#     outputs=gr.Textbox(label="Generated Goals"),
#     title="Exam Data Analysis Goals Generator",
#     description="Click on a goal in the visualization to generate related goals.",
#     allow_flagging="never",
#     theme="default",
#     css=html_content
# )

# if __name__ == "__main__":
#     iface.launch()
# from flask import Flask, request, jsonify, render_template_string
# import os
# from mistralai.client import MistralClient
# from mistralai.models.chat_completion import ChatMessage

# app = Flask(__name__)

# # Mistral AI setup
# api_key = os.getenv("MISTRAL_API_KEY")
# if not api_key:
#     raise ValueError("MISTRAL_API_KEY environment variable not set")

# model = "mistral-tiny"
# client = MistralClient(api_key=api_key)

# def generate_goals(input_var):
#     messages = [
#         ChatMessage(role="user", content=f"Generate 5 specific, industry relevant goals for {input_var} using Python and Pandas in exam data analysis. Each goal should include a brief name and a one-sentence description of the task or skill.")
#     ]
#     try:
#         response = client.chat(model=model, messages=messages)
#         return response.choices[0].message.content
#     except Exception as e:
#         return f"An error occurred: {str(e)}"

# html_content = """
# <!DOCTYPE html>
# <html lang="en">
# <head>
#     <meta charset="UTF-8">
#     <meta name="viewport" content="width=device-width, initial-scale=1.0">
#     <title>Exam Data Analysis Goals Generator</title>
#     <script src="https://d3js.org/d3.v7.min.js"></script>
#     <style>
#         #visualization { width: 100%; height: 600px; border: 1px solid #ccc; }
#         #generatedGoals { margin-top: 20px; padding: 10px; border: 1px solid #ccc; }
#     </style>
# </head>
# <body>
#     <h1>Exam Data Analysis Goals Generator</h1>
#     <div id="visualization"></div>
#     <div id="generatedGoals"></div>
#     <script>
#     const width = 1200;
#     const height = 800;
#     const goals = [
#             { id: 1, x: 100, y: 400, name: "Automate Data Import", description: "Develop scripts to automate exam data extraction from various sources (CSV, Excel, databases) using Pandas read_* functions." },
#             { id: 2, x: 200, y: 300, name: "Data Cleaning", description: "Implement robust data cleaning processes to handle missing values, outliers, and inconsistencies in exam data using Pandas methods like dropna(), fillna(), and apply()." },
#             { id: 3, x: 300, y: 200, name: "Data Transformation", description: "Utilize Pandas for complex data transformations such as pivoting exam results, melting question-wise scores, and creating derived features for analysis." },
#             { id: 4, x: 400, y: 300, name: "Statistical Analysis", description: "Develop functions to automate statistical analysis of exam results, including descriptive statistics, hypothesis testing, and correlation analysis using Pandas and SciPy." },
#             { id: 5, x: 500, y: 400, name: "Performance Metrics", description: "Create custom functions to calculate industry-standard exam performance metrics like item difficulty, discrimination index, and reliability coefficients using Pandas operations." },
#             { id: 6, x: 200, y: 500, name: "Data Filtering", description: "Implement advanced filtering techniques to segment exam data based on various criteria (e.g., demographic info, score ranges) using boolean indexing and query() method in Pandas." },
#             { id: 7, x: 300, y: 600, name: "Reporting Automation", description: "Develop automated reporting systems that use Pandas groupby() and agg() functions to generate summary statistics and performance reports for different exam cohorts." },
#             { id: 8, x: 400, y: 500, name: "Data Visualization", description: "Create interactive dashboards for exam data visualization using Pandas with Plotly or Bokeh, allowing stakeholders to explore results dynamically." },
#             { id: 9, x: 500, y: 600, name: "Time Series Analysis", description: "Implement time series analysis techniques using Pandas datetime functionality to track and forecast exam performance trends over multiple test administrations." },
#             { id: 10, x: 300, y: 400, name: "Data Integration", description: "Develop processes to merge exam data with other relevant datasets (e.g., student information systems, learning management systems) using Pandas merge() and join() operations." },
#             { id: 11, x: 600, y: 300, name: "Performance Optimization", description: "Improve the efficiency of Pandas operations on large exam datasets by utilizing techniques like chunking, multiprocessing, and query optimization." },
#             { id: 12, x: 700, y: 400, name: "Machine Learning Integration", description: "Integrate machine learning models with Pandas for predictive analytics, such as predicting exam success or identifying at-risk students based on historical data." },
#             { id: 13, x: 800, y: 500, name: "Custom Indexing", description: "Implement custom indexing strategies in Pandas to efficiently handle hierarchical exam data structures and improve data access patterns." },
#             { id: 14, x: 900, y: 400, name: "Data Anonymization", description: "Develop Pandas-based workflows to anonymize sensitive exam data, ensuring compliance with privacy regulations while maintaining data utility for analysis." },
#             { id: 15, x: 1000, y: 300, name: "Exam Item Analysis", description: "Create specialized functions using Pandas to perform detailed item analysis, including distractor analysis and reliability calculations for individual exam questions." },
#             { id: 16, x: 600, y: 500, name: "Longitudinal Analysis", description: "Implement Pandas-based methods for tracking student performance across multiple exams over time, identifying learning trends and progress patterns." },
#             { id: 17, x: 700, y: 600, name: "Adaptive Testing Analysis", description: "Develop analysis pipelines using Pandas to evaluate and optimize adaptive testing algorithms, including item selection strategies and scoring methods." },
#             { id: 18, x: 800, y: 700, name: "Exam Equating", description: "Create Pandas workflows to perform exam equating, ensuring comparability of scores across different versions or administrations of an exam." },
#             { id: 19, x: 900, y: 600, name: "Response Time Analysis", description: "Utilize Pandas to analyze exam response times, identifying patterns that may indicate guessing, test-taking strategies, or item difficulty." },
#             { id: 20, x: 1000, y: 500, name: "Collaborative Filtering", description: "Implement collaborative filtering techniques using Pandas to recommend study materials or practice questions based on exam performance patterns." },
#             { id: 21, x: 400, y: 700, name: "Exam Fraud Detection", description: "Develop anomaly detection algorithms using Pandas to identify potential exam fraud or unusual response patterns in large-scale testing programs." },
#             { id: 22, x: 500, y: 800, name: "Standard Setting", description: "Create Pandas-based tools to assist in standard setting processes, analyzing expert judgments and examinee data to establish performance standards." },
#             { id: 23, x: 600, y: 700, name: "Automated Reporting", description: "Implement automated report generation using Pandas and libraries like Jinja2 to create customized, data-driven exam reports for various stakeholders." },
#             { id: 24, x: 700, y: 800, name: "Cross-validation", description: "Develop cross-validation frameworks using Pandas to assess the reliability and generalizability of predictive models in educational assessment contexts." },
#             { id: 25, x: 800, y: 300, name: "API Integration", description: "Create Pandas-based interfaces to integrate exam data analysis workflows with external APIs, facilitating real-time data exchange and reporting." },
#             { id: 26, x: 900, y: 200, name: "Natural Language Processing", description: "Implement NLP techniques using Pandas and libraries like NLTK to analyze free-text responses in exams, enabling automated scoring and content analysis." },
#             { id: 27, x: 1000, y: 100, name: "Exam Blueprint Analysis", description: "Develop Pandas workflows to analyze exam blueprints, ensuring content coverage and alignment with learning objectives across multiple test forms." },
#             { id: 28, x: 100, y: 600, name: "Differential Item Functioning", description: "Implement statistical methods using Pandas to detect and analyze differential item functioning (DIF) in exams, ensuring fairness across different demographic groups." },
#             { id: 29, x: 200, y: 700, name: "Automated Feedback Generation", description: "Create Pandas-based systems to generate personalized feedback for test-takers based on their exam performance and identified areas for improvement." },
#             { id: 30, x: 300, y: 800, name: "Exam Security Analysis", description: "Develop analytical tools using Pandas to assess and enhance exam security, including analysis of item exposure rates and detection of potential security breaches." }
#         ];
#         const connections = [
#             { source: 1, target: 2 },
#             { source: 2, target: 3 },
#             { source: 3, target: 4 },
#             { source: 4, target: 5 },
#             { source: 5, target: 7 },
#             { source: 6, target: 7 },
#             { source: 7, target: 8 },
#             { source: 8, target: 9 },
#             { source: 9, target: 16 },
#             { source: 10, target: 13 },
#             { source: 11, target: 12 },
#             { source: 12, target: 20 },
#             { source: 13, target: 16 },
#             { source: 14, target: 21 },
#             { source: 15, target: 17 },
#             { source: 16, target: 18 },
#             { source: 17, target: 19 },
#             { source: 18, target: 22 },
#             { source: 19, target: 21 },
#             { source: 20, target: 29 },
#             { source: 21, target: 30 },
#             { source: 22, target: 23 },
#             { source: 23, target: 25 },
#             { source: 24, target: 12 },
#             { source: 25, target: 23 },
#             { source: 26, target: 15 },
#             { source: 27, target: 15 },
#             { source: 28, target: 22 },
#             { source: 29, target: 23 },
#             { source: 30, target: 21 },
#             // Additional connections for more interconnectivity
#             { source: 1, target: 10 },
#             { source: 2, target: 6 },
#             { source: 3, target: 13 },
#             { source: 4, target: 15 },
#             { source: 5, target: 28 },
#             { source: 8, target: 23 },
#             { source: 11, target: 25 },
#             { source: 14, target: 30 },
#             { source: 24, target: 17 },
#             { source: 26, target: 29 }
#         ];
#     const svg = d3.select("#visualization")
#         .append("svg")
#         .attr("width", width)
#         .attr("height", height);
#     const simulation = d3.forceSimulation(goals)
#         .force("link", d3.forceLink(connections).id(d => d.id))
#         .force("charge", d3.forceManyBody().strength(-400))
#         .force("center", d3.forceCenter(width / 2, height / 2));
#     const link = svg.append("g")
#         .selectAll("line")
#         .data(connections)
#         .enter().append("line")
#         .attr("stroke", "#999")
#         .attr("stroke-opacity", 0.6);
#     const node = svg.append("g")
#         .selectAll("circle")
#         .data(goals)
#         .enter().append("circle")
#         .attr("r", 10)
#         .attr("fill", d => d.color)
#         .call(d3.drag()
#             .on("start", dragstarted)
#             .on("drag", dragged)
#             .on("end", dragended));
#     const text = svg.append("g")
#         .selectAll("text")
#         .data(goals)
#         .enter().append("text")
#         .text(d => d.name)
#         .attr("font-size", "12px")
#         .attr("dx", 12)
#         .attr("dy", 4);
#     node.on("click", async function(event, d) {
#         const response = await fetch('/generate_goals', {
#             method: 'POST',
#             headers: { 'Content-Type': 'application/json' },
#             body: JSON.stringify({ input_var: d.name })
#         });
#         const data = await response.json();
#         document.getElementById("generatedGoals").innerHTML = `<h2>Generated Goals for ${d.name}</h2><pre>${data.goals}</pre>`;
#     });
#     simulation.on("tick", () => {
#         link
#             .attr("x1", d => d.source.x)
#             .attr("y1", d => d.source.y)
#             .attr("x2", d => d.target.x)
#             .attr("y2", d => d.target.y);
#         node
#             .attr("cx", d => d.x)
#             .attr("cy", d => d.y);
#         text
#             .attr("x", d => d.x)
#             .attr("y", d => d.y);
#     });
#     function dragstarted(event) {
#         if (!event.active) simulation.alphaTarget(0.3).restart();
#         event.subject.fx = event.subject.x;
#         event.subject.fy = event.subject.y;
#     }
#     function dragged(event) {
#         event.subject.fx = event.x;
#         event.subject.fy = event.y;
#     }
#     function dragended(event) {
#         if (!event.active) simulation.alphaTarget(0);
#         event.subject.fx = null;
#         event.subject.fy = null;
#     }
#     </script>
# </body>
# </html>
# """

# @app.route('/')
# def index():
#     return render_template_string(html_content)

# @app.route('/generate_goals', methods=['POST'])
# def generate_goals_api():
#     input_var = request.json['input_var']
#     goals = generate_goals(input_var)
#     return jsonify({'goals': goals})

# if __name__ == "__main__":
#     app.run(host='0.0.0.0', port=7860)
from http.server import HTTPServer, SimpleHTTPRequestHandler
from pyngrok import ngrok
import os
from mistralai.client import MistralClient
from mistralai.models.chat_completion import ChatMessage
import json

# Mistral AI setup
api_key = os.getenv("MISTRAL_API_KEY")
if not api_key:
    raise ValueError("MISTRAL_API_KEY environment variable not set")

model = "mistral-tiny"
client = MistralClient(api_key=api_key)

def generate_goals(input_var):
    messages = [
        ChatMessage(role="user", content=f"Generate 5 specific, industry relevant goals for {input_var} using Python and Pandas in exam data analysis. Each goal should include a brief name and a one-sentence description of the task or skill.")
    ]
    try:
        response = client.chat(model=model, messages=messages)
        return response.choices[0].message.content
    except Exception as e:
        return f"An error occurred: {str(e)}"

html_content = """
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Exam Data Analysis Goals Generator</title>
    <script src="https://d3js.org/d3.v7.min.js"></script>
    <style>
        #visualization { width: 100%; height: 600px; border: 1px solid #ccc; }
        #generatedGoals { margin-top: 20px; padding: 10px; border: 1px solid #ccc; }
    </style>
</head>
<body>
    <h1>Exam Data Analysis Goals Generator</h1>
    <div id="visualization"></div>
    <div id="generatedGoals"></div>
    <script>
    const width = 1200;
    const height = 800;
    const goals = [
        { id: 1, x: 100, y: 400, name: "Automate Data Import", description: "Develop scripts to automate exam data extraction from various sources (CSV, Excel, databases) using Pandas read_* functions." },
        { id: 2, x: 200, y: 300, name: "Data Cleaning", description: "Implement robust data cleaning processes to handle missing values, outliers, and inconsistencies in exam data using Pandas methods like dropna(), fillna(), and apply()." },
        { id: 3, x: 300, y: 200, name: "Data Transformation", description: "Utilize Pandas for complex data transformations such as pivoting exam results, melting question-wise scores, and creating derived features for analysis." },
        { id: 4, x: 400, y: 300, name: "Statistical Analysis", description: "Develop functions to automate statistical analysis of exam results, including descriptive statistics, hypothesis testing, and correlation analysis using Pandas and SciPy." },
        { id: 5, x: 500, y: 400, name: "Performance Metrics", description: "Create custom functions to calculate industry-standard exam performance metrics like item difficulty, discrimination index, and reliability coefficients using Pandas operations." },
        // Add more goals here...
    ];
    const connections = [
        { source: 1, target: 2 },
        { source: 2, target: 3 },
        { source: 3, target: 4 },
        { source: 4, target: 5 },
        // Add more connections here...
    ];
    const svg = d3.select("#visualization")
        .append("svg")
        .attr("width", width)
        .attr("height", height);
    const simulation = d3.forceSimulation(goals)
        .force("link", d3.forceLink(connections).id(d => d.id))
        .force("charge", d3.forceManyBody().strength(-400))
        .force("center", d3.forceCenter(width / 2, height / 2));
    const link = svg.append("g")
        .selectAll("line")
        .data(connections)
        .enter().append("line")
        .attr("stroke", "#999")
        .attr("stroke-opacity", 0.6);
    const node = svg.append("g")
        .selectAll("circle")
        .data(goals)
        .enter().append("circle")
        .attr("r", 10)
        .attr("fill", d => d.color || "#69b3a2")
        .call(d3.drag()
            .on("start", dragstarted)
            .on("drag", dragged)
            .on("end", dragended));
    const text = svg.append("g")
        .selectAll("text")
        .data(goals)
        .enter().append("text")
        .text(d => d.name)
        .attr("font-size", "12px")
        .attr("dx", 12)
        .attr("dy", 4);
    node.on("click", async function(event, d) {
        const response = await fetch('/generate_goals', {
            method: 'POST',
            headers: { 'Content-Type': 'application/json' },
            body: JSON.stringify({ input_var: d.name })
        });
        const data = await response.json();
        document.getElementById("generatedGoals").innerHTML = `<h2>Generated Goals for ${d.name}</h2><pre>${data.goals}</pre>`;
    });
    simulation.on("tick", () => {
        link
            .attr("x1", d => d.source.x)
            .attr("y1", d => d.source.y)
            .attr("x2", d => d.target.x)
            .attr("y2", d => d.target.y);
        node
            .attr("cx", d => d.x)
            .attr("cy", d => d.y);
        text
            .attr("x", d => d.x)
            .attr("y", d => d.y);
    });
    function dragstarted(event) {
        if (!event.active) simulation.alphaTarget(0.3).restart();
        event.subject.fx = event.subject.x;
        event.subject.fy = event.subject.y;
    }
    function dragged(event) {
        event.subject.fx = event.x;
        event.subject.fy = event.y;
    }
    function dragended(event) {
        if (!event.active) simulation.alphaTarget(0);
        event.subject.fx = null;
        event.subject.fy = null;
    }
    </script>
</body>
</html>
"""

class MyHandler(SimpleHTTPRequestHandler):
    def do_GET(self):
        self.send_response(200)
        self.send_header('Content-type', 'text/html')
        self.end_headers()
        self.wfile.write(html_content.encode())

    def do_POST(self):
        if self.path == '/generate_goals':
            content_length = int(self.headers['Content-Length'])
            post_data = self.rfile.read(content_length)
            data = json.loads(post_data.decode('utf-8'))
            input_var = data['input_var']
            goals = generate_goals(input_var)
            
            self.send_response(200)
            self.send_header('Content-type', 'application/json')
            self.end_headers()
            self.wfile.write(json.dumps({'goals': goals}).encode())
        else:
            self.send_error(404)

if __name__ == '__main__':
    port = 7860
    server = HTTPServer(('', port), MyHandler)
    public_url = ngrok.connect(port).public_url
    print(f" * ngrok tunnel \"{public_url}\" -> \"http://127.0.0.1:{port}\"")
    server.serve_forever()